Merge branch 'main' of cau-git.rz.uni-kiel.de:nt/mitarbeiter/silas/imdd_simulation

Classes/00_signals/Signal.m

@@ -344,10 +344,13 @@ classdef Signal
 
             arguments
                 obj
-                options.fignum
+                options.fignum = 2025
                 options.displayname = "";
                 options.color = [];
+                options.linestyle = '-';
                 options.normalizeToNyquist = 0;
+                options.addDCoffset = 0;
+                options.normalizeToDC = 0;
                 options.normalizeTo0dB = 0;
                 options.max_num_lines = []; % Leave empty or omit to disable line rotation
                 options.fft_length = [];
@@ -360,6 +363,8 @@ classdef Signal
                 options.fft_length = 2^(nextpow2(length(obj.signal))-9);
             end
 
+            
+
             if options.normalizeToNyquist == 0
                 [p_lin,f_Hz] = pwelch(obj.signal, hanning(options.fft_length), ...
                     options.fft_length/2, options.fft_length, ...
@@ -373,6 +378,8 @@ classdef Signal
                 % We'll keep f_rad for the x-axis in that mode.
             end
 
+            % p_lin = movmean(p_lin,4);
+
             if options.normalizeTo0dB
                 p_lin = p_lin ./ max(p_lin);
                 p_dbm = 10*log10(p_lin); % normalized to 0 dB
@@ -415,11 +422,20 @@ classdef Signal
             ax = gca;
             hold on
 
+            p_dbm = p_dbm+options.addDCoffset;
+
+            if options.normalizeToDC
+                [~,min_idx]=min(abs(f_GHz));
+                pow_at_dc = p_dbm(min_idx);
+                p_dbm = p_dbm-pow_at_dc;
+            end
+            % p_dbm = movmean(p_dbm,10);
+
             for s = 1:min(size(p_dbm))
                 if isempty(options.color)
                     plot(x_vec, p_dbm(:,s), 'DisplayName', options.displayname, 'LineWidth', 1);
                 else
-                    plot(x_vec, p_dbm(:,s), 'DisplayName', options.displayname, 'LineWidth', 1, 'Color', options.color);
+                    plot(x_vec, p_dbm(:,s), 'DisplayName', options.displayname, 'LineWidth', 1, 'Color', options.color,'LineStyle',options.linestyle);
                 end
             end
 
@@ -449,21 +465,25 @@ classdef Signal
 
             ylabel(ylab);
 
+            % --- Y-Axis scaling (auto with margin) ---
+            y_min = min(p_dbm(:));
+            y_max = max(p_dbm(:));
+
+            % Add 5% dynamic range margin on both sides
+            y_range = y_max - y_min;
+            if y_range == 0
+                y_range = 10; % fallback if flat
+            end
+
+            y_margin = 0.05 * y_range;
+            ylim([y_min - y_margin, y_max + y_margin]);
+
+            % Set ticks automatically, avoid overpopulation
             try
-                ylim([max(min(floor(min(p_dbm))-3, ax.YLim(1)),-40), min(max(ceil(max(p_dbm))+3, ax.YLim(2)),10)]);
-            catch
-                ylim([floor(min(p_dbm,[],'all'))-3, ceil(max(p_dbm,[],'all'))+3]);
+            yticks(round(linspace(y_min, y_max, min(10, max(4, ceil(y_range/10))))));
             end
-
-            if options.normalizeTo0dB
-                ylim([floor(min(p_dbm,[],'all'))-3, ceil(max(p_dbm,[],'all'))+3]);
-            else
-                ylim([floor(min(p_dbm,[],'all'))-3, ceil(max(p_dbm,[],'all'))+3]);
-            end
-
-            yticks(-200:10:10);
             grid on;
-            legend
+
 
         end
 
@@ -961,8 +981,8 @@ classdef Signal
                 maxA = max(sig(100:end-100))*1.3;
                 minA = min(sig(100:end-100))*1.3;
 
-                % maxA = 0.0015;
-                % minA = 0;
+                maxA = 0.12;
+                minA = -0.08;
 
                 difference= maxA-minA;
 
@@ -1012,7 +1032,7 @@ classdef Signal
 
                 % add information
 
-                if 1
+                if 0
 
                     pwr_dbm = round(obj.power,3);
                     pwr_lin = obj.power("unit",power_notation.W);
@@ -1121,20 +1141,20 @@ classdef Signal
 
                     end
 
-                    yticks(linspace(0,histpoints,6));
-                    y_tickstring = sprintfc('%.2f', y_tickstring);
-                    yticklabels(y_tickstring);
-
-                    xticks(linspace(0,histpoints_horizontal,6))
-                    x_tickstring = sprintfc('%.2f', linspace(0, 2/fsym, 8) .* 1e12);
-                    xticklabels(x_tickstring);
-
                     grid off
+
                 end
 
 
+                yticks(linspace(0,histpoints,6));
+                y_tickstring = sprintfc('%.2f', y_tickstring);
+                yticklabels(y_tickstring);
 
+                xticks(linspace(0,histpoints_horizontal,6))
+                x_tickstring = sprintfc('%.2f', linspace(0, 2/fsym, 8) .* 1e12);
+                xticklabels(x_tickstring);
 
+                % 
             end
 
             % disp('h');

Classes/01_transmit/ChannelFreqResp.m

@@ -139,6 +139,8 @@ classdef ChannelFreqResp < handle
             fnew = linspace(0,fstarget/2,length(Target)/2+1);
             fnew = fnew(2:end-1);
 
+
+
             % Old frequency axis (should be much coarser)
             idx_old  = find((obj.faxis > 0) .* (obj.faxis < fstarget/2)); %positions of all Frequencies smaller than fs/2
             int_fold = obj.faxis(idx_old); %old frequencies from 0 to fs/2
@@ -149,6 +151,10 @@ classdef ChannelFreqResp < handle
             % interpolate the frequency response that had a coarse frequency resolution (e.g. 256 bins) to the current frequency resolution (e.g. 21843 bins)
             iH  = interp1(int_fold, real(H_inv(idx_old)) ,fnew, 'linear') + 1i*interp1(int_fold, imag(H_inv(idx_old)) ,fnew, 'linear');
 
+            if 0
+                figure(7);hold on;plot(fnew,20*log10(abs(iH)))
+            end
+
             % set all NaN values to the fist/ last non-NaN value
             nH    = find(~isnan(iH),1,'first');
             iH(1:nH)=iH(nH);
@@ -171,6 +177,8 @@ classdef ChannelFreqResp < handle
             % five frequencies -> should be the vaue at f=0=DC component?
             iH  = iH./mean(abs(iH)); %why 1:5??
 
+            dc = 20*log10(abs(mean(abs(iH(1:5)))));
+
             % set maximum amplification
             % set als values higher than hmax to hmax and keep the
             % phase information by multiplication with respective
@@ -206,7 +214,7 @@ classdef ChannelFreqResp < handle
 
         function plot(obj)
 
-            figure(55);
+            figure();
             clf;
 
             Havg = obj.H;
@@ -225,7 +233,7 @@ classdef ChannelFreqResp < handle
             xlim([0.2 .5*max(obj.faxis)*1e-9]);
             grid on;
 
-            figure(56);
+            figure();
             clf;
 
 
@@ -246,7 +254,7 @@ classdef ChannelFreqResp < handle
             xlim([0.2 .5*max(obj.faxis)*1e-9]); grid on;
 
             %%% plot for publication
-            figure(1234);
+            figure(101);
             hold all;
             box on;
             title('Magnitude Freq. Response');
@@ -269,6 +277,25 @@ classdef ChannelFreqResp < handle
             legend('Interpreter','latex')
             grid on;
 
+            % 
+            figure(100); hold on;
+            Havg = obj.H;
+            Havg = Havg./max(Havg);
+            Hall = obj.H_all;
+
+            for i = 1:size(Hall,1)
+                Hall(i,:) = Hall(i,:)./max(Hall(i,:));
+            end
+
+            %1)
+            col = cbrewer2('Paired',8);
+            hold all;box on;title('Magnitude Freq. Response');
+            plot(obj.faxis/1e9, 20*log10(abs(Hall)),'linewidth',0.1,'LineStyle','-','Color',col(1,:),'HandleVisibility','off') ;
+            xlim([0.2 .5*max(obj.faxis)*1e-9]);
+            plot(obj.faxis/1e9, 20*log10(abs(Havg)),'LineWidth',2,'Color',col(2,:));
+            grid on;
+
+
 
         end
 

Classes/04_DSP/Equalizer/FFE.m

@@ -131,7 +131,7 @@ classdef FFE < handle
             mask = ones(obj.order,1);
             maincursor_pos=ceil(length(obj.e)/2);
             always_ideal_decision = 0;
-            save_debug = 1;
+            save_debug = 0;
             grad =0;
             weight = 0;
             update = 0;

Classes/04_DSP/Equalizer/FFE_MLSE.m

@@ -0,0 +1,233 @@
+classdef FFE_MLSE < handle
+    % Implementation of plain and simple FFE.
+    % 1) Training mode (stable performance when you use NLMS)
+    % 2) Decision directed mode
+
+    %LMS: mu in order of 0.0001 for acceptable convergence speed
+    %NLMS: mu in order of 0.01 for acceptable convergence speed
+    %RLS: mu is lambda -> 0.99 -> 1 (has a strong dependency on this! use a loop to find out best values)
+
+    %  FFE("epochs_tr",5,"epochs_dd",2,"len_tr",2^13,"mu_dd",mu_dd,"mu_tr",mu_tr,"order",25,"sps",2,"decide",0, "adaption",adaption_method(adaption),"dd_mode",use_dd_mode);
+
+    properties
+        sps % usually 2
+        order
+        e
+        e_tr
+        error
+
+        len_tr
+        mu_tr
+        epochs_tr
+
+        dd_mode % 1 or 0 to set DD-mode on or off
+        mu_dd %weight update in dd mode
+        epochs_dd
+
+        constellation
+
+        L %viterbi memory length
+
+        alpha
+        DIR
+        DIR_flip
+        trellis_states
+
+        traceback_depth
+    end
+
+    methods
+        function obj = FFE_MLSE(options)
+            arguments(Input)
+
+                options.sps = 2;
+                options.order = 15;
+
+                options.len_tr = 4096;
+                options.mu_tr = 0;
+                options.epochs_tr = 5;
+
+                options.dd_mode = 1;
+                options.mu_dd = 1e-5;
+                options.epochs_dd = 5;
+
+                options.traceback_depth = 1024;
+
+                options.L = 1
+
+            end
+
+            fn = fieldnames(options);
+            for n = 1:numel(fn)
+                obj.(fn{n}) = options.(fn{n});
+            end
+
+            obj.e = zeros(obj.order,1);
+            obj.error = 0;
+
+        end
+
+        function [X,X_viterbi] = process(obj, X, D)
+
+            % actual processing of the signal (steps 1. - 3.)
+            % 1 normalize RMS
+            X = X.normalize("mode","rms");
+
+            obj.constellation = unique(D.signal);
+
+            if length(X)/length(D) ~= obj.sps
+                warning('Signal length does not fit to reference!');
+            end
+
+            % Training Mode
+            n = obj.len_tr;
+            training = 1;
+            obj.equalize(X.signal, D.signal,obj.mu_tr,obj.epochs_dd,n,training);
+            obj.e_tr = obj.e;
+
+            % Decision Directed Mode
+            n = X.length;
+            training = 0;
+            [y,y_vit]=obj.equalize(X.signal, D.signal,obj.mu_dd,obj.epochs_dd,n,training);
+
+            X_viterbi = X;
+
+            X.signal = y;
+            X.fs = D.fs; %change sampling frequency of outgoing signal from fdac e.g. 2 sps to symbol spaced = fsym
+            lbdesc = [num2str(obj.order),' tap FFE'];
+            X  = X.logbookentry(lbdesc); % append to logbook
+
+            X_viterbi.signal = y_vit;
+            X_viterbi.fs = D.fs; %change sampling frequency of outgoing signal from fdac e.g. 2 sps to symbol spaced = fsym
+            lbdesc = [num2str(obj.order),'order FFE + PF + Viterbi'];
+            X_viterbi  = X_viterbi.logbookentry(lbdesc); % append to logbook
+
+
+        end
+
+        function [y,y_vit] = equalize(obj,x,d,mu,epochs,N,training)
+            % ==============================================================
+            %  FFE + Whitening + Viterbi Equalizer (reference implementation)
+            % ==============================================================
+
+            % --- Input padding and preallocation
+            x = [zeros(floor(obj.order/2),1); x; zeros(obj.order,1)];
+            N_ = N / obj.sps;
+            y = zeros(N_,1);
+            y_white = zeros(N_,1);
+
+            for epoch = 1:epochs
+
+                % ==============================================================
+                %  INITIALIZATION (only before final epoch and detection mode)
+                % ==============================================================
+                if epoch == epochs && ~training
+
+                    % --- Parameters
+                    S = numel(unique(d));
+                    L = obj.L;
+                    nStates = S^L;
+                    nFeasible = S^(L-1)*S;
+
+                    % --- Trellis setup
+                    obj.DIR       = arburg(y-d, L);
+                    obj.DIR_flip  = flip(obj.DIR);
+                    obj.trellis_states = reshape(unique(d),1,[]);
+
+                    pre_comb_mat  = repmat(obj.trellis_states, L, 1);
+                    pre_comb_cell = mat2cell(pre_comb_mat, ones(1,L), size(pre_comb_mat,2));
+                    combs         = fliplr(combvec(pre_comb_cell{:}).');
+                    first_sym     = combs(:,1);
+                    last_sym      = combs(:,end);
+                    nStates       = size(combs,1);
+
+                    noise_free_received = inf(nStates,nStates);
+                    valid = false(nStates);
+
+                    for from = 1:nStates
+                        for to = 1:nStates
+                            if all(combs(to,2:end) == combs(from,1:end-1))
+                                noise_free_received(to,from) = ...
+                                    dot(combs(to,:), obj.DIR_flip(end:-1:2)) + last_sym(from)*obj.DIR_flip(1);
+                                valid(to,from) = true;
+                            end
+                        end
+                    end
+
+                    nf_vec = noise_free_received(valid);
+                    [valid_to, valid_from] = find(valid);
+                    from_per_to = arrayfun(@(to)find(valid(to,:)), 1:nStates, 'UniformOutput', false);
+
+                    % --- Noise stats
+                    y_ideal = conv(d(:), obj.DIR(:), "same");
+                    sigma2  = mean(abs(y - y_ideal).^2);
+                    inv2s2  = 1/(2*sigma2);
+
+                    % --- Vector initialization
+                    bm_vec  = zeros(1,nFeasible);
+                    pm      = zeros(nStates,1);
+                    pm_next = zeros(nStates,1);
+                    bm_fw   = zeros(nStates,nStates,length(y));
+                    zi      = zeros(max(numel(obj.DIR)-1,0),1);
+                end
+
+                % ==============================================================
+                %  RUNTIME LOOP (FFE update + Viterbi detection in last epoch)
+                % ==============================================================
+                symbol = 0;
+                for sample = 1:obj.sps:N
+                    symbol = symbol + 1;
+
+                    % --- FFE output
+                    U = x(obj.order+sample-1:-1:sample);
+                    y(symbol,1) = obj.e.' * U;
+
+                    % --- Decision
+                    if training
+                        d_hat(symbol,1) = d(symbol);
+                    else
+                        [~,symbol_idx]  = min(abs(y(symbol) - obj.constellation));
+                        d_hat(symbol,1) = obj.constellation(symbol_idx);
+                    end
+
+                    % --- LMS weight update
+                    err(symbol) = d_hat(symbol) - y(symbol);
+                    obj.e = obj.e + mu * (err(symbol) * U);
+
+                    % --- Whitening + Viterbi (final epoch only)
+                    if epoch == epochs && ~training
+
+                        [y_white(symbol), zi] = filter(obj.DIR,1,y(symbol), zi);
+
+                        if symbol == 1
+                            pm = -inf(nStates,nStates);
+                            pm(:,1:nStates) = 0;
+                        else
+                            bm_vec = -(y_white(symbol) - nf_vec).^2 * inv2s2;
+                            bm_mat = -inf(nStates,nStates);
+                            bm_mat(valid) = bm_vec;
+
+                            pm_new = pm + bm_mat;
+                            [pm_survive(:,symbol), pm_survivor_fw_idx(:,symbol)] = max(pm_new,[],2);
+                            pm = repmat(pm_survive(:,symbol).', nStates,1);
+                        end
+
+                        % --- Traceback
+                        if mod(symbol,obj.traceback_depth) == 0
+                            [~,viterbi_path(symbol)] = max(pm_survive(:,symbol));
+                            for n = symbol:-1:symbol-obj.traceback_depth+2
+                                viterbi_path(n-1) = pm_survivor_fw_idx(viterbi_path(n),n);
+                            end
+                        end
+                    end
+                end
+
+                % --- Final reconstruction
+                if epoch == epochs && ~training
+                    y_vit = first_sym(viterbi_path);
+                end
+            end
+        end
+
+    end
+end

Classes/04_DSP/Equalizer/ML_MLSE.m

@@ -0,0 +1,355 @@
+classdef ML_MLSE < handle
+    % ---------------------------------------------------------------------
+    % W. Lanneer and Y. Lefevre,
+    % “Machine Learning-Based Pre-Equalizers for Maximum Likelihood
+    %  Sequence Estimation in High-Speed PONs,” EUSIPCO 2023
+    % ---------------------------------------------------------------------
+    % This implementation reproduces the closed-loop ML-based
+    % pre-equalizer training for MLSE, supporting both training and
+    % detection (decision-directed) modes.
+    % ---------------------------------------------------------------------
+
+    properties
+        sps
+        order
+        e
+        e_tr
+        error
+
+        len_tr
+        mu_tr
+        epochs_tr
+
+        dd_mode
+        mu_dd
+        epochs_dd
+        adaptive_mu
+
+        constellation
+        L
+        alpha
+        DIR
+        DIR_flip
+        trellis_states
+        traceback_depth
+        delta
+
+        % Internal variables
+        S
+        Nf
+        nStates
+        nFeasible
+        combs
+        first_sym
+        last_sym
+        valid
+        valid_to_idx
+        valid_from_idx
+        w
+
+        % Fast lookup
+        nSym
+        key_table
+        trans_index
+        true_to_state_idx
+
+        % Debug metrics
+        ber = []
+        ce  = ones(1,1)
+    end
+
+    methods
+        function obj = ML_MLSE(options)
+            arguments(Input)
+                options.sps = 2;
+                options.order = 15;
+                options.len_tr = 4096;
+                options.mu_tr = 0.001;
+                options.epochs_tr = 5;
+                options.dd_mode = 1;
+                options.mu_dd = 1e-5;
+                options.epochs_dd = 5;
+                options.adaptive_mu = 1;
+                options.delta = 0;
+                options.traceback_depth = 1024;
+                options.L = 1;
+            end
+
+            fn = fieldnames(options);
+            for n = 1:numel(fn)
+                obj.(fn{n}) = options.(fn{n});
+            end
+
+            obj.e = zeros(obj.order,1);
+            obj.error = 0;
+        end
+
+        % ==============================================================
+        % PROCESS
+        % ==============================================================
+        function [X,X_viterbi] = process(obj, X, D)
+            % Normalize input RMS
+            X = X.normalize("mode","rms");
+            obj.constellation = sort(unique(D.signal),'ascend');
+            obj.nSym = numel(obj.constellation);
+
+            if length(X)/length(D) ~= obj.sps
+                warning('Signal length does not fit to reference!');
+            end
+
+            % --- Parameters
+            obj.S = obj.nSym;
+            obj.Nf = obj.order * obj.sps;
+            obj.nStates = obj.S^obj.L;
+            obj.nFeasible = obj.nStates * obj.S;
+
+            % --- Trellis mapping
+            obj.trellis_states = reshape(obj.constellation,1,[]);
+            pre_comb_mat  = repmat(obj.trellis_states, obj.L, 1);
+            pre_comb_cell = mat2cell(pre_comb_mat, ones(1,obj.L), size(pre_comb_mat,2));
+            obj.combs     = fliplr(combvec(pre_comb_cell{:}).');
+            obj.first_sym = obj.combs(:,1);
+            obj.last_sym  = obj.combs(:,end);
+            obj.nStates   = size(obj.combs,1);
+
+            % --- Valid transitions
+            obj.valid = false(obj.nStates);
+            for from = 1:obj.nStates
+                for to = 1:obj.nStates
+                    if all(obj.combs(to,2:end) == obj.combs(from,1:end-1))
+                        obj.valid(to,from) = true;
+                    end
+                end
+            end
+            [obj.valid_to_idx,obj.valid_from_idx] = find(obj.valid);
+
+            % --- Initialize weights
+            if isempty(obj.w) || any(size(obj.w) ~= [obj.Nf+1,obj.nFeasible])
+                obj.w = randn(obj.Nf+1,obj.nFeasible);
+            end
+
+            % --- Fast lookup tables
+            [~, sym_idx_mat] = ismember(obj.combs, obj.constellation);
+            key_vals = 1 + sum((sym_idx_mat - 1) .* (obj.nSym .^ (0:obj.L-1)), 2);
+            max_key = obj.nSym^obj.L;
+            obj.key_table = zeros(max_key,1,'uint32');
+            obj.key_table(key_vals) = 1:obj.nStates;
+
+            obj.trans_index = sparse(obj.nStates,obj.nStates);
+            for i = 1:length(obj.valid_from_idx)
+                f = obj.valid_from_idx(i);
+                t = obj.valid_to_idx(i);
+                obj.trans_index(t,f) = i;
+            end
+
+            % ==============================================================
+            % TRAINING
+            % ==============================================================
+            fprintf('\n--- Training mode ---\n');
+            obj.equalize(X.signal, D.signal, obj.mu_tr, obj.epochs_tr, obj.len_tr, true);
+            obj.e_tr = obj.e;
+
+            % ==============================================================
+            % DECISION-DIRECTED / TESTING
+            % ==============================================================
+            fprintf('--- Decision-directed / detection mode ---\n');
+            [y, y_vit] = obj.equalize(X.signal, D.signal, obj.mu_dd, obj.epochs_dd, X.length, false);
+
+            X_viterbi = X;
+            X.signal = y;
+            X_viterbi.signal = y_vit;
+        end
+
+        % ==============================================================
+        % EQUALIZE
+        % ==============================================================
+        function [y,y_ref] = equalize(obj,x,d,mu,epochs,N,training)
+            debug = 0;
+            showPlots = 0;
+            y = zeros(N,1);
+            nSymbols = ceil(N/obj.sps);
+
+            for epoch = 1:epochs
+                pm = zeros(obj.nStates,1);
+                pred = zeros(nSymbols,obj.nStates,'uint32');
+                pm_sto = nan(obj.nStates,nSymbols,'like',pm);
+                CE_accum = 0;
+
+                start_sample = 1;
+                end_sample   = N;
+                start_symbol = 1 + floor((start_sample - 1)/obj.sps);
+
+                % --- initialize true state
+                if numel(d) >= obj.L && start_symbol >= obj.L
+                    init_seq = d(start_symbol-obj.L+1:start_symbol);
+                    key_init = obj.seq2key(init_seq);
+                    true_to_state_idx = obj.key_table(key_init);
+                    if true_to_state_idx==0, true_to_state_idx=1; end
+                else
+                    true_to_state_idx = uint32(1);
+                end
+
+                for sample = start_sample:obj.sps:end_sample
+                    symbol = (sample - start_sample)/obj.sps + 1;
+                    sym_idx = start_symbol + (symbol - 1);
+
+                    % --- Observation window (with delta)
+                    i1 = sample - obj.Nf + 1 + obj.delta;
+                    i2 = sample + obj.delta;
+                    buf = x(max(1,i1):min(length(x),i2));
+                    padL = max(0,1 - i1);
+                    padR = max(0,i2 - length(x));
+                    yk = [zeros(padL,1); buf(:); zeros(padR,1)];
+                    yk = [yk;1];
+
+                    % --- Branch metrics
+                    c_hat = (yk.' * obj.w).';
+                    pm = pm - min(pm);
+                    v_tilde = pm(obj.valid_from_idx) + c_hat;
+
+                    % --- allocate once
+                    if epoch==1 && symbol==1
+                        obj.true_to_state_idx = ones(ceil(N/obj.sps),1,'uint32');
+                    end
+
+                    % --- previous "to" becomes "from"
+                    if symbol>1
+                        true_from_state_idx = obj.true_to_state_idx(symbol-1);
+                    else
+                        true_from_state_idx = 1;
+                    end
+
+                    % --- compute or reuse "to" state
+                    if epoch==1
+                        if sym_idx>=obj.L
+                            key_to = obj.seq2key(d(sym_idx-obj.L+1:sym_idx));
+                            state_idx = obj.key_table(key_to);
+                            if state_idx==0
+                                state_idx = true_from_state_idx;
+                            end
+                            obj.true_to_state_idx(symbol) = state_idx;
+                        else
+                            obj.true_to_state_idx(symbol) = true_from_state_idx;
+                        end
+                    end
+                    true_to_state_idx = obj.true_to_state_idx(symbol);
+
+                    % --- fast Dirac creation
+                    dirac = zeros(obj.nFeasible,1);
+                    trans_idx = obj.trans_index(true_to_state_idx,true_from_state_idx);
+                    if trans_idx~=0
+                        dirac(trans_idx)=1;
+                    end
+
+                    % ===================================================================
+                    % TRAINING MODE (weight update)
+                    % ===================================================================
+                    if training
+                        % --- Softmax and CE
+                        v_shift = -(v_tilde - min(v_tilde));
+                        v_shift = min(v_shift,100);
+                        expv = exp(v_shift);
+                        p = expv./(sum(expv)+eps);
+                        CE_symbol(symbol) = -log(p(dirac==1)+eps);
+
+                        % --- CE smoothing and adaptive μ
+                        if sym_idx>obj.L
+                            CE_smooth(symbol)=0.01*CE_symbol(symbol)+0.99*CE_symbol(symbol-1);
+                        else
+                            CE_smooth(symbol)=CE_symbol(symbol);
+                        end
+                        CE_accum=CE_accum+CE_symbol(symbol);
+
+                        % --- Gradient update
+                        dmp=(dirac-p)';
+                        dL_Dw=(yk).*dmp;
+                        if sym_idx>=obj.L
+                            if obj.adaptive_mu
+                                mu_eff=CE_smooth(symbol);
+                                mu_eff=max(min(mu_eff,0.2),1e-4);
+                            else
+                                mu_eff=mu;
+                            end
+                            obj.w=obj.w - mu_eff.*dL_Dw;
+                        end
+                    end
+
+                    % ===================================================================
+                    % DECODING MODE (Viterbi only)
+                    % ===================================================================
+                    % Compare-Select (always executed)
+                    vmat=inf(obj.nStates,obj.nStates);
+                    vmat(obj.valid)=v_tilde;
+                    [pm_next,pred(symbol,:)]=min(vmat,[],2);
+                    pm_next=pm_next-min(pm_next);
+                    pm=pm_next;
+                    pm_sto(:,symbol)=pm;
+                end
+
+                % --- Traceback
+                [~,s_end]=min(pm);
+                vpath=zeros(symbol,1,'uint32');
+                vpath(symbol)=s_end;
+                for n=symbol:-1:2
+                    vpath(n-1)=pred(n,vpath(n));
+                end
+
+                y_ref=d(start_symbol:end);
+                y=obj.first_sym(vpath);
+
+                % --- BER/CE reporting and plots
+                if training
+                    err=sum(y~=y_ref(1:length(y)));
+                    ser=err/length(y);
+                    try
+                        ref_bits=PAMmapper(obj.S,0).demap(y_ref(1:length(y)));
+                        eq_bits=PAMmapper(obj.S,0).demap(y);
+                        [~,~,ber,~]=calc_ber(ref_bits,eq_bits,"skip_front",10,"skip_end",10,"returnErrorLocation",1);
+                        fprintf('Epoch %d - BER: %.2e\n',epoch,ber);
+                        obj.ber(epoch)=ber;
+                    catch
+                        fprintf('Epoch %d - SER: %.2e\n',epoch,ser);
+                        obj.ber(epoch)=ser;
+                    end
+                    obj.ce(epoch)=CE_accum/symbol;
+
+                    if debug && mod(epoch,10)==1 && showPlots
+                        figure(10);clf
+                        subplot(3,2,1:2);
+                        imagesc(obj.w);axis xy;colorbar;title('Filter W');
+                        subplot(3,2,3);
+                        vtilde_mat=NaN(obj.nStates,obj.nStates);
+                        vtilde_mat(obj.valid)=v_tilde;
+                        imagesc(vtilde_mat);axis xy;colorbar;title('Path Metrics (v\_tilde)');
+                        subplot(3,2,4);
+                        plot(1:symbol,pm_sto);title('Path Metric Evolution');
+                        subplot(3,2,5);hold on;
+                        scatter(1:symbol,CE_symbol,1,'.');
+                        scatter(1:symbol,CE_smooth,1,'.');
+                        title('Cross Entropy');
+                        subplot(3,2,6);hold on;
+                        yyaxis left
+                        scatter(1:length(obj.ce),obj.ce,10,'s','filled');
+                        ylabel('Cross Entropy');
+                        yyaxis right
+                        scatter(1:length(obj.ber),obj.ber,10,'d','filled');
+                        set(gca,'YScale','log');
+                        ylabel('BER (log)');
+                        xlabel('Epoch');grid on;
+                        title('Convergence');
+                        drawnow;
+                    end
+                end
+            end
+        end
+
+        % ==============================================================
+        % Helper: Sequence → key (always scalar)
+        % ==============================================================
+        function key = seq2key(obj, seq)
+            [~, idx] = ismember(flip(seq), obj.constellation);
+            pow = (obj.nSym .^ (0:obj.L-1)).';
+            key = 1 + sum((idx(:) - 1) .* pow);
+        end
+    end
+end

Classes/04_DSP/Sequence Detection/MLSE.m

@@ -6,6 +6,10 @@ classdef MLSE < handle
         DIR
         trellis_states
         duobinary_output
+        trellis_state_mode
+        trellis_exclusion
+        debug
+        scale_mode
     end
 
     methods (Access=public)
@@ -19,7 +23,10 @@ classdef MLSE < handle
                 options.DIR double = [1];
                 options.trellis_states double = [-3 -1 1 3];
                 options.duobinary_output logical = false;
-
+                options.trellis_state_mode = 2;
+                options.trellis_exclusion = 0;
+                options.scale_mode = 2;
+                options.debug = 0;
             end
 
             %
@@ -33,6 +40,12 @@ classdef MLSE < handle
 
         function [signalclass_hd,LLR,GMI] = process(obj,signalclass,ref_symbolclass)
 
+            arguments
+                obj
+                signalclass
+                ref_symbolclass
+            end
+
             data_in =   signalclass.signal;
             shape_in = size(data_in);
             data_ref = ref_symbolclass.signal;
@@ -54,18 +67,25 @@ classdef MLSE < handle
 
         function [VITERBI_ESTIMATION_SYMBOLS,LLR_maxlogmap,GMI] = process_(obj,data_in,data_ref)
 
-            debug                   = 0;
             
-            trellis_state_mode      = 2;   % General: States should match the target states of the prev. EQ (EQ's job was to reduce the error between signal and the target)
+            arguments
+                obj
+                data_in
+                data_ref
+            end
+
+            debug                   = obj.debug;
+
+            trellis_state_mode      = obj.trellis_state_mode;   % General: States should match the target states of the prev. EQ (EQ's job was to reduce the error between signal and the target)
                                             % 0 = use provided states (MUST provide the correct states); 
                                             % 1 = normalize to = 1 rms;
                                             % 2 = use target symbols; 
                                             % 3 = use statistical levels
                                             % 3 analyzes avg of rx signal levels - can help with nonlinear impairments
             
-            trellis_exclusion       = 0; % PAM-6 only (only if data is NOT precoded!)
+            trellis_exclusion       = obj.trellis_exclusion; % PAM-6 only (only if data is NOT precoded!)
             
-            scale_mode              = 2; % scale_mode: 
+            scale_mode              = obj.scale_mode; % scale_mode: 
                                             % 0 = no scaling, 
                                             % 1 = RMS→scale MODEL, 
                                             % 2 = MMSE/time-corr→scale MODEL, 
@@ -98,7 +118,7 @@ classdef MLSE < handle
 
             elseif trellis_state_mode == 2 %simply use the states from the ref signal (should be a robust option)
 
-                obj.trellis_states = reshape(unique(data_ref),size(obj.trellis_states));
+                obj.trellis_states = reshape(unique(data_ref),1,length(unique(data_ref)));
 
             elseif trellis_state_mode == 3 %use_statistical_levels
 
@@ -257,14 +277,107 @@ classdef MLSE < handle
 
 
             if debug
-                alpha_ = alpha - min(alpha) + eps;
-                figure();hold on;
-                n = 10;
-                scatter(1:n,obj.trellis_states(repmat([1:numel(obj.trellis_states)]',1,n)),abs(alpha_(:,end-n+1:end)),'Marker','o','LineWidth',1);
-                scatter(1:n,obj.trellis_states(viterbi_path(end-n+1:end)),500,'Marker','x','LineWidth',1,'MarkerEdgeColor','green');
-                % scatter(1:n,data_ref(end-n+1:end),500,'Marker','x','LineWidth',1,'MarkerEdgeColor','red');
-                yticks(obj.trellis_states);
-                ylim([min(obj.trellis_states)-1 max(obj.trellis_states)+1]);
+                % alpha_ = alpha - min(alpha) + eps;
+                % figure();hold on;
+                % n = 10;
+                % scatter(1:n,obj.trellis_states(repmat([1:numel(obj.trellis_states)]',1,n)),abs(alpha_(:,end-n+1:end)),'Marker','o','LineWidth',1);
+                % scatter(1:n,obj.trellis_states(viterbi_path(end-n+1:end)),500,'Marker','x','LineWidth',1,'MarkerEdgeColor','green');
+                % % scatter(1:n,data_ref(end-n+1:end),500,'Marker','x','LineWidth',1,'MarkerEdgeColor','red');
+                % yticks(obj.trellis_states);
+                % ylim([min(obj.trellis_states)-1 max(obj.trellis_states)+1]);
+
+                %% ----- Build true path from known sequence (data_ref) -----
+                % Memory length used by VA (L = length(obj.DIR)-1 symbols stored in state)
+                L = size(combs,2);                 % each row of combs is the L-tap state vector
+                N = length(data_in);
+
+                % Map each trellis level to an index (1..M)
+                [~, level_to_idx] = ismember(levels, levels); %#ok<ASGLU>  % identity map
+                [ok_ref, ref_idx] = ismember(data_ref(:).', levels);
+                if ~all(ok_ref)
+                    warning('Some data_ref symbols are not in "levels". True-path build may fail.');
+                end
+
+                % Precompute next_state(from_state, u_idx) LUT such that:
+                % combs(next_state,:) == [ combs(from_state,2:end) , levels(u_idx) ]
+                next_state = zeros(size(combs,1), numel(levels), 'uint32');
+                for from = 1:size(combs,1)
+                    prefix = combs(from,2:end);  % what must match in 'to' for a valid transition
+                    for ui = 1:numel(levels)
+                        target = [prefix, levels(ui)];
+                        % find the unique 'to' whose state vector equals target
+                        to = find(all(bsxfun(@eq, combs, target), 2), 1, 'first');
+                        if isempty(to), to = 0; end
+                        next_state(from, ui) = to;
+                    end
+                end
+
+                % Initialize true path at n=1: pick any state with last_sym == data_ref(1)
+                % Prefer one whose suffix matches the first available history if L>1.
+                cand = find(last_sym == data_ref(1));
+                if isempty(cand)
+                    % fallback: choose closest in amplitude (should not happen if levels match)
+                    [~,ix] = min(abs(last_sym - data_ref(1)));
+                    cand = ix;
+                end
+                true_state_path = zeros(1,N,'uint32');
+                true_state_path(1) = cand(1);
+
+                % Propagate forward using the known inputs data_ref(n)
+                for n = 2:N
+                    ui = ref_idx(n);     % index of the actual transmitted level at time n
+                    from = true_state_path(n-1);
+                    if from==0 || ui==0
+                        true_state_path(n) = 0;
+                    else
+                        true_state_path(n) = next_state(from, ui);
+                        if true_state_path(n)==0
+                            % Safety fallback: if no valid transition found (should not happen)
+                            % choose any to-state whose vector matches shift+current symbol
+                            target = [combs(from,2:end), levels(ui)];
+                            to = find(all(bsxfun(@eq, combs, target), 2), 1, 'first');
+                            if isempty(to), to = from; end
+                            true_state_path(n) = to;
+                        end
+                    end
+                end
+
+                %% ----- Collect branch metrics along decoded vs. true path -----
+                bm_decoded = nan(1,N);
+                bm_true    = nan(1,N);
+
+                % n=1 in your code stores pm into bm_fw(:,:,1); real BMs start at n>=2
+                for n = 2:N
+                    % Decoded path: to = viterbi_path(n), from = survivor that fed it
+                    to_d   = viterbi_path(n);
+                    from_d = pm_survivor_fw_idx(to_d, n);
+                    bm_decoded(n) = bm_fw(to_d, from_d, n);
+
+                    % True path: transition true_state_path(n-1) -> true_state_path(n)
+                    to_t   = true_state_path(n);
+                    from_t = true_state_path(n-1);
+                    if to_t>0 && from_t>0
+                        bm_true(n) = bm_fw(to_t, from_t, n);
+                    end
+                end
+
+                % Convert to "cost" for intuitive plotting (your BM is a log-likelihood)
+                cost_dec = -bm_decoded;
+                cost_true= -bm_true;
+
+                %% ----- Plot a short window for clarity -----
+                win = max(2, N-20000):N;  % last 200 samples (adjust as needed)
+                figure('Color','w'); hold on; grid on; box on;
+                plot(win, cost_true(win),  'LineWidth',1.2, 'DisplayName','True path cost (−BM)');
+                plot(win, cost_dec(win),   'LineWidth',1.2, 'DisplayName','Decoded path cost (−BM)');
+                xlabel('Time index n'); ylabel('Branch cost'); title('Branch metrics along true vs decoded path');
+                legend('Location','best');
+
+                %% ----- Optional: overlay symbol levels for the same window -----
+                yyaxis right
+                plot(win, data_in(win), ':', 'LineWidth',0.8, 'DisplayName','y(n)');
+                ylabel('Amplitude');
+
             end
 
             VITERBI_ESTIMATION_SYMBOLS(1:length(data_in)) = first_sym(viterbi_path);
@@ -544,11 +657,6 @@ classdef MLSE < handle
 
         end
 
-        function s = logsumexp(a,dim)
-            % returns log(sum(exp(a),dim)) safely
-            amax = max(a,[],dim);
-            s    = amax + log(sum(exp(a - amax), dim));
-        end
 
     end
 end

Classes/05_Lab/Exfo_laser.m

@@ -12,6 +12,7 @@ classdef Exfo_laser < handle
         safety_mode
     end
 
+    %% Any user can call these methods to alter the laser state
     methods (Access=public)
 
         function obj = Exfo_laser(options)
@@ -169,6 +170,10 @@ classdef Exfo_laser < handle
         end
 
     end %public mehtods
+
+
+    % DAU must not be able to call these funcitons outside of the class!
+    % Hence they are private! :-) 
     methods (Access=private)
 
         function v = connectLaser_(obj)
@@ -180,7 +185,7 @@ classdef Exfo_laser < handle
                 configureTerminator(v, "CR", "CR");  % Set the terminator to carriage return (CR)
             elseif obj.lab_interface == lab_interface.gpib
                 % Connect via GPIB adress
-                visastring = ['GPIB0::',obj.connection_id,'::INSTR'];
+                visastring = ['GPIB1::',obj.connection_id,'::INSTR'];
                 v = visadev(visastring);
             else
                 error('Interface not supported');
@@ -342,8 +347,13 @@ classdef Exfo_laser < handle
             command = ['CH', num2str(channel), ':L=', num2str(desiredWavelength_nm)];
             writeline(serialObj, command);
 
+            if abs(obj.cur_wavelength - desiredWavelength_nm)>30
+                pause(5); %it takes time to tune the laser! If this is not here it WILL break!
+            else
+                pause(1.5);
+            end
             if serialObj.Type == visalib.InterfaceType.serial
-                pause(0.2);
+                pause(1.5);
                 response = readline(serialObj);
                 success = contains(response, 'OK');
             end

Classes/05_Lab/LabDeviceTemplate.m

@@ -0,0 +1,83 @@
+classdef LabDeviceTemplate < handle
+
+    % Minimal template for lab device control via VISA / SCPI
+    % Copy this file and adapt:
+    %   - visaAddress
+    %   - public API methods
+    %   - private SCPI commands
+
+    properties (Access = public)
+        active logical = true
+    end
+
+    properties (Access = protected)
+        visaAddress string
+    end
+
+    methods (Access = public)
+
+        function obj = LabDeviceTemplate(options)
+            arguments
+                options.active logical = true
+                options.visaAddress string = ""
+            end
+
+            % apply options
+            fn = fieldnames(options);
+            for k = 1:numel(fn)
+                obj.(fn{k}) = options.(fn{k});
+            end
+
+            % optional: quick connectivity check
+            if obj.active
+                v = obj.connect();
+                writeline(v,"*IDN?");
+                idn = readline(v);
+                disp("Connected to: " + string(idn));
+                delete(v);
+            end
+        end
+
+
+        function success = set(obj, value)
+            % Example public API
+            v = obj.connect();
+            success = obj.setValue_(v, value);
+            delete(v);
+        end
+
+
+        function value = read(obj)
+            % Example readback
+            v = obj.connect();
+            value = obj.readValue_(v);
+            delete(v);
+        end
+
+    end
+
+
+    methods (Access = protected)
+
+        function v = connect(obj)
+            assert(obj.visaAddress ~= "", "No VISA address defined");
+            v = visadev(obj.visaAddress);
+        end
+
+    end
+
+
+    methods (Access = private)
+
+        function success = setValue_(~, v, value)
+            writeline(v, sprintf("SET %g", value));
+            success = true;
+        end
+
+        function value = readValue_(~, v)
+            writeline(v, "READ?");
+            value = str2double(readline(v));
+        end
+
+    end
+end

Classes/05_Lab/OSA_Advantest.m

@@ -0,0 +1,327 @@
+classdef OSA_Advantest < handle
+    % Advantest Q8384 Optical Spectrum Analyzer (GPIB)
+    % User inputs in nanometers
+
+    % EXAMPLE:
+
+    % span_nm     = 70;
+    % lambda_c_nm = 1310;
+    % osa = OSA_Advantest( ...
+    %     'span_nm',        span_nm, ...
+    %     'lambda_c_nm',    lambda_c_nm, ...
+    %     'resolution_nm',  0.1, ...
+    %     'sampling_points', 10001 );
+    %
+    % osa.configure();
+    % [lambda_nm, psd_dBm] = osa.measure();
+    % figure()
+    % plot(lambda_nm,psd_dBm);
+
+    properties (Access = public)
+        span_nm double = 5
+        resolution_nm double = 0.05
+        lambda_c_nm double = 1550
+        sampling_points double = 1001
+        timeout_s double = 10
+
+        lambda_nm
+        psd_dBm
+    end
+
+    properties (Constant)
+        valid_resolutions_nm = [0.01 0.02 0.05 0.10 0.20 0.50]
+        valid_sampling_points = [101 201 501 1001 2001 5001 10001];
+    end
+
+    properties (Access = protected)
+        visaAddress string = "GPIB0::3::INSTR"
+    end
+
+    methods
+        function obj = OSA_Advantest(options)
+            arguments
+                options.visaAddress string = "GPIB0::3::INSTR"
+                options.span_nm double = 5
+                options.resolution_nm double = 0.05
+                options.lambda_c_nm double = 1550
+                options.sampling_points double = 1001
+                options.timeout_s double = 10
+            end
+
+            fn = fieldnames(options);
+            for k = 1:numel(fn)
+                obj.(fn{k}) = options.(fn{k});
+            end
+
+            assert(ismember(obj.resolution_nm, obj.valid_resolutions_nm), ...
+                'Invalid resolution. Allowed: 0.01 | 0.02 | 0.05 | 0.10 | 0.20 | 0.50 nm');
+            assert(ismember(obj.sampling_points, obj.valid_sampling_points), ...
+                'Invalid # of Samp. Points. Allowed: 101 201 501 1001 2001 5001 10001');
+
+            v = obj.connect();
+            writeline(v,"*IDN?");
+            disp(readline(v));
+            delete(v);
+        end
+
+        function configure(obj)
+            assert(ismember(obj.resolution_nm, obj.valid_resolutions_nm), ...
+                'Invalid resolution. Allowed: 0.01 | 0.02 | 0.05 | 0.10 | 0.20 | 0.50 nm');
+            assert(ismember(obj.sampling_points, obj.valid_sampling_points), ...
+                'Invalid # of Samp. Points. Allowed: 101 201 501 1001 2001 5001 10001');
+
+            v = obj.connect();
+
+            writeline(v,"FMT0,HED0,SDL0");
+            writeline(v,"BUZ0");
+            writeline(v,"QUI0");
+
+            writeline(v, sprintf("RES %gnm", obj.resolution_nm));
+            writeline(v, sprintf("SPT %g",  obj.sampling_points));
+            writeline(v, sprintf("CEN %gnm", obj.lambda_c_nm));
+            writeline(v, sprintf("SPA %gnm", obj.span_nm));
+            writeline(v,"SWE 0");
+
+            delete(v);
+        end
+
+        function [osa_x_nm, osa_y_dBm] = measure(obj)
+            v = obj.connect();
+
+            writeline(v,"CSB");
+            writeline(v,"MEA1");
+
+            active = 1; cnt = 0;
+            while active
+                pause(1);
+                writeline(v,"MEA?");
+                active = str2double(readline(v));
+                cnt = cnt + 1;
+                if cnt > obj.timeout_s
+                    delete(v);
+                    error("OSA_Q8384:Timeout","Measurement timeout");
+                end
+            end
+
+            writeline(v,"PKL");
+
+            writeline(v,"ODN?");
+            readline(v); % number of points (not strictly needed)
+
+            writeline(v,"FMT0,HED0,SDL0");
+
+            writeline(v,"OSD0");           % Y-axis
+            osa_y_dBm = str2num(readline(v));
+
+            writeline(v,"OSD1");           % X-axis
+            osa_x_m = str2num(readline(v));
+            osa_x_nm =  osa_x_m.*1e9;
+            delete(v);
+
+            obj.lambda_nm=osa_x_nm;
+            obj.psd_dBm=osa_y_dBm;
+
+        end
+
+        % --- Replace / extend plot() in OSA_Q8384 class ---
+
+        function plot(obj, options)
+            arguments
+                obj
+                options.FigureNumber double = 1
+                options.Hold logical = true
+                options.Title string = "OSA Spectrum"
+                options.LineWidth double = 0.8
+                options.Grid logical = true
+                options.Color (1,3) double = [0 0.4470 0.7410]
+                options.DisplayName string = ""
+            end
+
+            if isempty(obj.psd_dBm) || isempty(obj.lambda_nm)
+                error('Record spectrum first: run osa.measure()');
+            end
+
+            fig = figure(options.FigureNumber);
+            fig.Color = 'w';
+
+            ax = gca;
+            if ~options.Hold
+                cla(ax);
+            else
+                hold(ax,'on');
+            end
+
+            plot(ax, obj.lambda_nm, obj.psd_dBm, ...
+                'LineWidth', options.LineWidth, ...
+                'Color', options.Color, ...
+                'DisplayName', options.DisplayName);
+
+            xlabel(ax,'Wavelength [nm]');
+            ylabel(ax,'Optical Power [dBm]');
+
+            if options.Title ~= ""
+                title(ax, options.Title);
+            end
+
+            xlim(ax,[min(obj.lambda_nm) max(obj.lambda_nm)]);
+            yl = ylim(ax);
+            ylim(ax,[yl(1)-1 yl(2)+1]);
+
+            if options.Grid
+                grid(ax,'on');
+                grid(ax,'minor');
+            end
+
+            set(ax, ...
+                'FontSize',11, ...
+                'LineWidth',1, ...
+                'Box','on');
+
+            if options.DisplayName ~= ""
+                legend(ax,'show','Location','best');
+            end
+        end
+
+        function osnr = computeOSNR(obj, options)
+            % OSNR computation based on ratio method (RBW-independent)
+            % Assumes obj.lambda_nm [nm], obj.psd_dBm [dBm/RBW] exist
+
+            arguments
+                obj
+                options.bw_signal_nm double = 0.1    % channel bandwidth (0.1 for CW?!?!)
+                options.ref_bw_nm double = 0.1       % OSNR reference bandwidth
+                options.carrier_frequency = NaN;
+                options.noise_guard_nm = 1;
+                options.noise_window_nm =1;
+                options.debugplots = 0;
+            end
+
+            assert(~isempty(obj.lambda_nm) && ~isempty(obj.psd_dBm), ...
+                'Run measure() before computing OSNR');
+
+            obj.lambda_nm = obj.lambda_nm;
+            psd    = obj.psd_dBm;
+
+            % --- find carrier wavelength recorded in OSA ---
+            % options.carrier_frequency [nm], options.noise_guard_nm, options.noise_window_nm
+
+            if ~isnan(options.carrier_frequency)
+
+                % local search window around expected CW carrier
+                idx_search = obj.lambda_nm > (options.carrier_frequency - options.noise_guard_nm) & ...
+                    obj.lambda_nm < (options.carrier_frequency + options.noise_guard_nm);
+
+                [~, imax_local] = max(psd(idx_search));
+                lambda_search = obj.lambda_nm(idx_search);
+                lambda_c = lambda_search(imax_local);
+                [~,imax] = find(obj.lambda_nm==lambda_c);
+
+                % define local noise windows directly adjacent to signal (guarded)
+                idx_noise_left  = obj.lambda_nm > (lambda_c - options.noise_guard_nm - options.noise_window_nm) & ...
+                    obj.lambda_nm < (lambda_c - options.noise_guard_nm);
+
+                idx_noise_right = obj.lambda_nm > (lambda_c + options.noise_guard_nm) & ...
+                    obj.lambda_nm < (lambda_c + options.noise_guard_nm + options.noise_window_nm);
+
+                idx_noise = idx_noise_left | idx_noise_right;
+
+            else
+                % fallback: global maximum (unsafe for pathological ASE cases)
+                [~, imax] = max(psd);
+                lambda_c = obj.lambda_nm(imax);
+
+                % ASE from outer quarters of spectrum
+                N = numel(obj.lambda_nm);
+                idx_noise = false(size(obj.lambda_nm));
+                idx_noise([3:round(N/4), round(3*N/4):N-1]) = true;
+            end
+
+            % --- extract signal window ---
+            idx_signal = obj.lambda_nm > (lambda_c - options.bw_signal_nm/2) & ...
+                obj.lambda_nm < (lambda_c + options.bw_signal_nm/2);
+
+            if ~any(idx_signal)
+                idx_signal(imax) = true;
+            end
+
+            cc_lambda = obj.lambda_nm(idx_signal);
+            cc_psd    = psd(idx_signal);
+
+            % --- estimate ASE from local noise (flat ASE assumption) ---
+            noise_lambda = obj.lambda_nm(idx_noise);
+            noise_psd    = psd(idx_noise);
+
+
+            ase_psd = interp1(noise_lambda, noise_psd, cc_lambda, ...
+                'linear', 'extrap');
+
+            % --- convert to linear (mW / RBW) ---
+            sig_lin = 10.^(cc_psd/10);
+            ase_lin = 10.^(ase_psd/10);
+
+            % --- integrate (RBW cancels) ---
+            P_sig_lin = sum(sig_lin);
+            P_ase_lin = sum(ase_lin);
+
+            P_sig_dB = 10*log10(P_sig_lin);
+            P_ase_dB = 10*log10(P_ase_lin);
+
+            osnr.p_sig_db = P_sig_dB;
+            osnr.p_ase_db = P_ase_dB;
+
+            % --- OSNR direct from peak value to the noise estimate out of band ---
+            osnr.direct_dB = psd(imax) - 10*log10(mean(ase_lin)) ;
+
+            % --- OSNR in measurement bandwidth ---
+            osnr.measured_dB = P_sig_dB - P_ase_dB;
+
+            % --- normalize to reference bandwidth (usually 0.1 nm) ---
+            osnr.ref_dB = osnr.measured_dB + ...
+                10*log10(options.bw_signal_nm / options.ref_bw_nm);
+
+            % --- ASE-corrected OSNR (optional definition) ---
+            osnr.corrected_dB = ...
+                10*log10(10^(P_sig_dB/10) - 10^(P_ase_dB/10)) - P_ase_dB;
+
+            osnr.corrected_ref_dB = osnr.corrected_dB + ...
+                10*log10(options.bw_signal_nm / options.ref_bw_nm);
+
+            % metadata
+            osnr.lambda_c_nm = lambda_c;
+            osnr.bw_signal_nm = options.bw_signal_nm;
+            osnr.ref_bw_nm = options.ref_bw_nm;
+
+            if options.debugplots
+                figure(100)
+                xline(lambda_c,'LineWidth',2,'DisplayName','Max. OSA Value','Color',[0.8,0.8,0.8])
+                obj.plot("DisplayName",'OSNR','FigureNumber',100);
+                yline(osnr.p_ase_db,'LineWidth',2,'DisplayName','Signal Estimation');
+                yline(osnr.p_sig_db,'LineWidth',2,'DisplayName','Noise Estimation');
+                scatter(obj.lambda_nm(idx_noise),obj.psd_dBm(idx_noise),'Marker','.','LineWidth',4,'DisplayName','Noise Samples','MarkerEdgeColor','red');
+                scatter(obj.lambda_nm(idx_signal),obj.psd_dBm(idx_signal),'Marker','.','LineWidth',4,'DisplayName','Signal Samples','MarkerEdgeColor','green');
+                scatter(obj.lambda_nm(idx_signal),ase_psd,'Marker','.','LineWidth',4,'DisplayName','Signal Samples','MarkerEdgeColor','magenta');
+                ylim([min(obj.psd_dBm), max([obj.psd_dBm,osnr.p_sig_db+5])]);
+
+                figure(101);hold on
+                xline(lambda_c,'LineWidth',2,'DisplayName','Max. OSA Value','Color',[0.8,0.8,0.8])
+                xline(lambda_c - options.bw_signal_nm/2,'LineWidth',2,'DisplayName','CW - 0.5 BW','Color',[0.6,0.6,0.8])
+                xline(lambda_c + options.bw_signal_nm/2,'LineWidth',2,'DisplayName','CW + 0.5 BW','Color',[0.6,0.8,0.8])
+                obj.plot("DisplayName",'OSNR','FigureNumber',101);
+                yline(osnr.p_ase_db,'LineWidth',2,'DisplayName','Signal Estimation');
+                yline(osnr.p_sig_db,'LineWidth',2,'DisplayName','Noise Estimation');
+                scatter(obj.lambda_nm(idx_signal),obj.psd_dBm(idx_signal),'Marker','.','LineWidth',4,'DisplayName','Signal Samples','MarkerEdgeColor','green');
+                scatter(obj.lambda_nm(idx_signal),ase_psd,'Marker','.','LineWidth',4,'DisplayName','Signal Samples','MarkerEdgeColor','magenta');
+                xlim([lambda_c - options.bw_signal_nm, lambda_c + options.bw_signal_nm])
+                ylim([min(obj.psd_dBm), max([obj.psd_dBm,osnr.p_sig_db+5])]);
+            end
+
+        end
+
+    end
+
+    methods (Access = protected)
+        function v = connect(obj)
+            v = visadev(obj.visaAddress);
+        end
+    end
+end

Classes/05_Lab/OptLaserN7714A.m

@@ -0,0 +1,146 @@
+classdef OptLaserN7714A < handle
+    % Minimal controller for Keysight N7714A tunable laser over VISA-LAN
+    % Methods: getLaserInfo, setPower, setWavelength
+    %
+    % Examples:
+    %   L = OptLaserN7714A('TCPIP::192.168.0.50::INSTR');
+    %   L.setWavelength(1550);       % nm
+    %   L.setPower(-3.0, true);      % dBm, also turns output ON
+    %   info = L.getLaserInfo();
+
+    properties (Access=private)
+        v               % visadev handle
+        resource char   % VISA resource string, e.g. 'TCPIP::...::INSTR'
+    end
+
+    methods
+        function obj = OptLaserN7714A(resource)
+            arguments
+                resource (1,:) char
+            end
+            obj.resource = resource;
+
+            % Robust connect with small retry loop
+            maxRetries = 3; pauseTime = 1.0; lastErr = [];
+            for k = 1:maxRetries
+                try
+                    obj.v = visadev(obj.resource);
+                    % Identify instrument (SCPI *IDN?)
+                    writeline(obj.v, '*IDN?');     % SCPI common, identification
+                    idn = strtrim(readline(obj.v)); %#ok<NASGU>
+                    break
+                catch ME
+                    lastErr = ME;
+                    pause(pauseTime);
+                end
+            end
+            if isempty(obj.v) || ~isvalid(obj.v)
+                error('OptLaserN7714A:ConnectFailed', ...
+                    'Could not connect to %s. Last error: %s', obj.resource, lastErr.message);
+            end
+
+            % Set power unit to dBm for SOURce tree to be explicit (optional)
+            % [:SOUR]:POWer:UNIT dBm  (laser power unit)
+            % Ref: SOURce:POWer:UNIT. :contentReference[oaicite:0]{index=0}
+            try
+                writeline(obj.v, 'SOUR:POW:UNIT DBM');
+            catch
+                % non-fatal
+            end
+        end
+
+        function delete(obj)
+            if ~isempty(obj.v) && isvalid(obj.v); delete(obj.v); end
+        end
+
+        function setPower(obj, power_dBm, outputOn, channel)
+            arguments
+                obj
+                power_dBm (1,1) double
+                outputOn (1,1) logical = true
+                channel (1,1) double {mustBeMember(channel,0:4)} = 1
+            end
+
+            v = obj.v;
+            cmd = sprintf('SOUR%d:POW:LEV:IMM:AMPL %g DBM', channel, power_dBm);
+            writeline(v, cmd);
+
+            if outputOn
+                writeline(v, sprintf('SOUR%d:POW:STAT 1', channel)); %SOUR1:POW:STAT 0
+            else
+                writeline(v, sprintf('SOUR%d:POW:STAT 0', channel));
+            end
+
+            writeline(v, '*OPC?');
+            a=readline(v);
+        end
+
+        function setWavelength(obj, wavelength_nm, channel)
+            arguments
+                obj
+                wavelength_nm (1,1) double {mustBePositive}
+                channel (1,1) double {mustBeMember(channel,0:4)} = 1
+            end
+            v = obj.v;
+            cmd = sprintf('SOUR%d:WAV %gNM', channel, wavelength_nm);
+            writeline(v, cmd);
+            writeline(v, '*OPC?'); readline(v);
+        end
+
+        function setDither(obj, enable, port)
+            % Enable or disable laser frequency dither (stabilization).
+            % enable = false → no dither (FIX), true → stabilization (STAB).
+            % port = 1..4 (default 1).
+            if nargin < 3
+                port = 1;
+            end
+
+            v = obj.v;
+            if enable
+                writeline(v, sprintf('SOUR%d:CONTrol:FCDither ON', port)); %[:SOURce[n]][:CHANnel[m]]:CONTrol:FCDither
+            else
+                writeline(v, sprintf('SOUR%d:CONTrol:FCDither OFF', port));
+            end
+
+            % optional confirmation
+            writeline(v, sprintf('SOUR%d:CONTrol:FCDither?', port));
+            modeStr = strtrim(readline(v));
+            fprintf('Port %d dither mode set to %s\n', port, modeStr);
+        end
+
+
+
+        function info = getLaserInfo(obj, port)
+            % Returns struct with IDN, output state, wavelength (nm), power (dBm)
+            % port = 1..4 (for N7714A with 4 outputs). Default = 1.
+            if nargin < 2
+                port = 1;
+            end
+
+            v = obj.v;
+
+            % *IDN?
+            writeline(v, '*IDN?');
+            idn = strtrim(readline(v));
+
+            % Output state: :SOURn:POW:STAT?
+            writeline(v, sprintf('SOUR%d:POW:STAT?', port));
+            out_state = str2double(readline(v)) ~= 0;
+
+            % Wavelength query (returns meters → convert to nm)
+            writeline(v, sprintf('SOUR%d:WAV?', port));
+            wav_m = str2double(readline(v));
+            wav_nm = wav_m * 1e9;
+
+            % Power (set level) query (dBm)
+            writeline(v, sprintf('SOUR%d:POW:LEV:IMM:AMPL?', port));
+            p_dBm = str2double(readline(v));
+
+            info = struct('idn', idn, ...
+                'output_on', out_state, ...
+                'wavelength_nm', wav_nm, ...
+                'power_dBm', p_dBm);
+        end
+
+    end
+end

Classes/05_Lab/OptPowerMeter8153A.m

@@ -0,0 +1,168 @@
+classdef OptPowerMeter8153A < handle
+
+    % Code for HEWLETT-PACKARD 8153A Optical Power Meter (2-channel).
+    % https://www.artisantg.com/info/Agilent_8153A_Manual.pdf?srsltid=AfmBOoqTe38sbGRNHUFXjw5uO6Br7I7aSfgAdRpx-hVvM4sTIhFYyD5x
+    % PAGE 166++
+
+    % Typical usage:
+    %   pm = HPPowerMeter8153A( ...
+    %       'active',        [false true], ...
+    %       'wavelength_nm', [1550 1310] );
+    %
+    %   pm.setWavelength([1550 1310]);   % only CH2 is modified
+    %   p = pm.readPower();              % reads only active channels
+
+
+    properties (Access = public)
+        active        logical = [true true]      % channel enable
+        wavelength_nm double  = [1550 1550]      % per-channel wavelength [nm]
+        power_dBm     double  = [NaN NaN]        % last read power
+        offset_dB double      = [0 0]    % per-channel power offset [dB]
+        avgTime_s double        = [0.2 0.2];
+    end
+
+    properties (Constant)
+        avgTime_min_s = 0.02      % 20 ms
+        avgTime_max_s = 3600      % 1 hour
+    end
+
+    properties (Access = protected)
+        visaAddress string = "GPIB1::22::INSTR"
+        numChannels double = 2
+    end
+
+    methods
+        function obj = OptPowerMeter8153A(options)
+            arguments
+                options.visaAddress string = "GPIB1::22::INSTR"
+                options.active logical = [true true]
+                options.wavelength_nm double = [1550 1550]
+            end
+
+            fn = fieldnames(options);
+            for k = 1:numel(fn)
+                obj.(fn{k}) = options.(fn{k});
+            end
+
+            assert(numel(obj.active)==obj.numChannels)
+            assert(numel(obj.wavelength_nm)==obj.numChannels)
+
+            v = obj.connect();
+            writeline(v,"*IDN?");
+            disp(readline(v));
+            pause(0.1);
+            delete(v);
+            pause(0.1);
+
+            obj.setWavelength(obj.wavelength_nm);
+        end
+
+        function setWavelength(obj, lambda_nm)
+            if nargin > 1
+                obj.wavelength_nm = lambda_nm;
+            end
+
+            v = obj.connect();
+            for ch = 1:obj.numChannels
+                if obj.active(ch)
+                    writeline(v, ...
+                        sprintf("SENS%d:POW:WAVE %gNM", ch, obj.wavelength_nm(ch)));
+                end
+            end
+            pause(0.1);
+            delete(v);
+            pause(0.1);
+        end
+
+        function p = readPower(obj)
+            v = obj.connect();
+            for ch = 1:obj.numChannels
+                if obj.active(ch)
+                    writeline(v, sprintf("READ%d:POW?", ch));
+                    obj.power_dBm(ch) = str2double(readline(v));
+                else
+                    obj.power_dBm(ch) = NaN;
+                end
+            end
+            delete(v);
+            p = obj.power_dBm;
+        end
+
+        function setOffsetCorrection(obj, offset_dB)
+            obj.offset_dB = offset_dB;
+
+            v = obj.connect();
+            for ch = 1:obj.numChannels
+                if obj.active(ch)
+                    writeline(v, ...
+                        sprintf("SENS%d:CORR:LOSS:INP:MAGN %gDB", ch, obj.offset_dB(ch)));
+                end
+            end
+            delete(v);
+        end
+
+        function offset_dB = getOffsetCorrection(obj)
+            v = obj.connect();
+            offset_dB = NaN(1,obj.numChannels);
+            for ch = 1:obj.numChannels
+                if obj.active(ch)
+                    writeline(v, sprintf("SENS%d:CORR:LOSS:INP:MAGN?", ch));
+                    offset_dB(ch) = str2double(readline(v));
+                end
+            end
+            delete(v);
+            obj.offset_dB = offset_dB;
+        end
+
+        function setAvgTime(obj, avgTime_s)
+
+            if any(avgTime_s < obj.avgTime_min_s) || any(avgTime_s > obj.avgTime_max_s)
+
+                oldv = avgTime_s;
+
+                avgTime_s = max(avgTime_s, obj.avgTime_min_s);
+                avgTime_s = min(avgTime_s, obj.avgTime_max_s);
+
+                fprintf('[HPPowerMeter8153A] Averaging time clipped:\n');
+                for ch = 1:obj.numChannels
+                    if oldv(ch) ~= avgTime_s(ch)
+                        fprintf('  CH%d: %.3g s  →  %.3g s  (allowed %.3g … %.3g s)\n', ...
+                            ch, oldv(ch), avgTime_s(ch), ...
+                            obj.avgTime_min_s, obj.avgTime_max_s);
+                    end
+                end
+            end
+
+            obj.avgTime_s = avgTime_s;
+
+            v = obj.connect();
+            for ch = 1:obj.numChannels
+                if obj.active(ch) && ~isnan(obj.avgTime_s(ch))
+                    writeline(v, ...
+                        sprintf("SENS%d:POW:ATIME %g", ch, obj.avgTime_s(ch)));
+                end
+            end
+            delete(v);
+        end
+
+        function avgTime_s = getAvgTime(obj)
+            v = obj.connect();
+            avgTime_s = NaN(1,obj.numChannels);
+            for ch = 1:obj.numChannels
+                if obj.active(ch)
+                    writeline(v, sprintf("SENS%d:POW:ATIME?", ch));
+                    avgTime_s(ch) = str2double(readline(v));
+                end
+            end
+            delete(v);
+            obj.avgTime_s = avgTime_s;
+        end
+
+    end
+
+    methods (Access = protected)
+        function v = connect(obj)
+            v = visadev(obj.visaAddress);
+        end
+    end
+end

Classes/05_Lab/Thor_PDFA.m

@@ -20,7 +20,7 @@ classdef Thor_PDFA < handle
         function obj = Thor_PDFA(options)
 
             arguments
-                options.serialport_number = 'COM12'
+                options.serialport_number = 'COM18'
                 options.safety_mode = 1;
             end
 
@@ -37,7 +37,7 @@ classdef Thor_PDFA < handle
         end
 
         function o = connectSerial(obj)
-
+            o = [];
             try
                 % Connect to the PDFA
                 o = serialport(obj.serialport_number, 9600);
@@ -62,11 +62,15 @@ classdef Thor_PDFA < handle
         function getStatus(obj)
 
             o = obj.connectSerial();
-            obj.getStatus_(o);
-            if obj.safety_mode
-                obj
+
+            if ~isempty(o)
+                obj.getStatus_(o);
+                if obj.safety_mode
+                    obj
+                end
             end
 
+
         end
 
         function enablePDFA(obj)

Classes/DataBaseHandler/DBHandler.m

@@ -51,7 +51,7 @@ classdef DBHandler < handle
                         "Vendor", "MySQL", ...
                         "Server", "134.245.243.254", ...
                         "PortNumber", 3306, ...
-                        "JDBCDriverLocation", "C:\Users\Silas\Documents\mysql-connector-j-9.3.0\mysql-connector-j-9.3.0.jar");
+                        "JDBCDriverLocation", "C:\Users\sioe\Documents\mysql-connector-j-9.3.0\mysql-connector-j-9.3.0.jar");
                 end
 
             catch e
@@ -588,14 +588,13 @@ classdef DBHandler < handle
                     return
                 catch ME
                     if attempt < maxFast
-                        pause(1)
+                        pause(0.1)
                     else
-                        pause(10)
+                        pause(1)
                     end
                     lastErr = ME;
                 end
             end
-            pause(60)
             error('Database fetch failed after %d attempts:\n%s', maxSlow, lastErr.getReport())
         end
 

Classes/DataBaseHandler/Metricstruct.m

@@ -16,9 +16,9 @@ classdef Metricstruct
         SNR (1,1) double {mustBeNumeric} = NaN
         SNR_level (:,1) double {mustBeNumeric} = []
         STD (1,1) double {mustBeNumeric} = NaN
-        STD_level (:,1) double {mustBeNumeric, mustBeNonnegative} = []
+        STD_level (:,1) double = []
         STDrx (1,1) double {mustBeNumeric} = NaN
-        STDrx_level (:,1) double {mustBeNumeric, mustBeNonnegative} = []
+        STDrx_level (:,1) double  = []
         EVM (1,1) double {mustBeNumeric} = NaN
         EVM_level (:,1) double {mustBeNumeric} = []
 
@@ -71,16 +71,22 @@ classdef Metricstruct
             end
         end
 
-        function print(obj)
+        function print(obj,options)
             % Print method to display key metrics in a formatted way
+            arguments
+                obj 
+                options.description = ''; 
+            end
+
 
             % Define the width for formatting
             nameWidth = 15;  % Width for parameter names
             valueWidth = 12; % Width for values
 
             % Print header
+            
             fprintf('\n%s\n', repmat('=', 1, nameWidth + valueWidth));
-            fprintf(' Results \n');
+            fprintf([char(options.description),' Results \n']);
             fprintf('%s\n', repmat('=', 1, nameWidth + valueWidth));
 
             % Function to format numbers with appropriate precision

Classes/GifWriter.m

@@ -0,0 +1,146 @@
+classdef GifWriter < handle
+    %GIFWRITER  Simple class to create GIFs from figures (parallel-safe)
+    %
+    % Example:
+    %   g = GifWriter('Name','mySim','Parallel',true);
+    %   parfor i = 1:10
+    %       plot(rand(10,1));
+    %       g.addFrame(1,i);
+    %   end
+    %   g.compile(1);
+
+    properties
+        Name (1,:) char = 'default'   % GIF base name
+        DelayTime (1,1) double = 0.1  % Frame delay in seconds
+        Parallel (1,1) logical = false % Enable parallel-safe mode
+        BaseDir (1,:) char            % Base directory for temp frames
+        OutputDir (1,:) char          % Final GIF output directory
+    end
+
+    methods
+        %% Constructor
+        function obj = GifWriter(varargin)
+            % Parse name/value pairs
+            p = inputParser;
+            addParameter(p, 'Name', 'default', @ischar);
+            addParameter(p, 'DelayTime', 0.1, @isnumeric);
+            addParameter(p, 'Parallel', false, @islogical);
+            addParameter(p, 'OutputDir', fullfile(pwd, 'gif_output'), @ischar);
+            parse(p, varargin{:});
+
+            obj.Name      = p.Results.Name;
+            obj.DelayTime = p.Results.DelayTime;
+            obj.Parallel  = p.Results.Parallel;
+            obj.OutputDir = p.Results.OutputDir;
+            obj.BaseDir   = fullfile(obj.OutputDir, 'tmp', obj.Name);
+
+            if ~exist(obj.BaseDir, 'dir'), mkdir(obj.BaseDir); end
+            if ~exist(obj.OutputDir, 'dir'), mkdir(obj.OutputDir); end
+        end
+
+        %%
+        function addFrame(obj, figInput, pos)
+            %ADDFRAME  Add a figure frame to the GIF (supports parallel mode)
+            %
+            % Usage:
+            %   obj.addFrame(figHandle)
+            %   obj.addFrame(figNum)
+            %   obj.addFrame(figHandle, pos)    % parallel mode
+            %   obj.addFrame(figNum, pos)
+            %
+            % In parallel mode, 'pos' must be a unique integer (loop index).
+
+            if nargin < 3, pos = []; end
+
+            % --- Resolve figure handle ---
+            if isnumeric(figInput)
+                % User passed a figure number
+                if ~ishandle(figInput)
+                    warning('GifWriter:addFrame', 'Figure %d not found.', figInput);
+                    return;
+                end
+                figHandle = figure(figInput);
+            elseif isa(figInput, 'matlab.ui.Figure')
+                figHandle = figInput;
+            else
+                error('GifWriter:addFrame:InvalidInput', ...
+                    'Input must be a figure handle or figure number.');
+            end
+
+            % --- Parallel-safe frame writing ---
+            if obj.Parallel
+                if isempty(pos)
+                    error('GifWriter:ParallelMode', ...
+                        'In parallel mode, provide a unique ''pos'' identifier.');
+                end
+
+                % Directory for this figure number
+                frameDir = fullfile(obj.BaseDir, sprintf('fig_%d', figHandle.Number));
+                if ~exist(frameDir, 'dir')
+                    mkdir(frameDir);
+                end
+
+                % File path for this frame
+                frameFile = fullfile(frameDir, sprintf('frame_%05d.png', pos));
+
+                % Export to PNG (headless-safe)
+                exportgraphics(figHandle, frameFile, 'Resolution', 150);
+
+            else
+                % --- Serial mode: append directly to GIF ---
+                gifFile = fullfile(obj.OutputDir, ...
+                    sprintf('%s_fig_%d.gif', obj.Name, figHandle.Number));
+
+                % Export frame temporarily
+                tmpFile = [tempname, '.png'];
+                exportgraphics(figHandle, tmpFile, 'Resolution', 150);
+                img = imread(tmpFile);
+                delete(tmpFile);
+
+                % Append to GIF
+                [A, map] = rgb2ind(img, 256);
+                if ~isfile(gifFile)
+                    imwrite(A, map, gifFile, 'gif', ...
+                        'LoopCount', Inf, 'DelayTime', obj.DelayTime);
+                else
+                    imwrite(A, map, gifFile, 'gif', ...
+                        'WriteMode', 'append', 'DelayTime', obj.DelayTime);
+                end
+            end
+        end
+
+
+
+        %% Compile all PNGs into a GIF (and clean up)
+        function compile(obj, fignum)
+            figDir = fullfile(obj.BaseDir, sprintf('fig_%d', fignum));
+            gifFile = fullfile(obj.OutputDir, sprintf('%s_fig_%d.gif', obj.Name, fignum));
+
+            frames = dir(fullfile(figDir, 'frame_*.png'));
+            if isempty(frames)
+                warning('GifWriter:NoFrames', 'No frames found for figure %d.', fignum);
+                return;
+            end
+
+            % Sort by frame name
+            [~, idx] = sort({frames.name});
+            frames = frames(idx);
+
+            % Combine into a GIF
+            for i = 1:numel(frames)
+                img = imread(fullfile(frames(i).folder, frames(i).name));
+                [A, map] = rgb2ind(img, 256);
+                if i == 1
+                    imwrite(A, map, gifFile, 'gif', ...
+                        'LoopCount', Inf, 'DelayTime', obj.DelayTime);
+                else
+                    imwrite(A, map, gifFile, 'gif', ...
+                        'WriteMode', 'append', 'DelayTime', obj.DelayTime);
+                end
+            end
+
+            % Clean up temporary frames
+            rmdir(figDir, 's');
+        end
+    end
+end

Classes/Warehouse_class/classes/DataStorage.m

@@ -137,7 +137,21 @@ classdef DataStorage < handle
                     if ~isempty(tmp)
 
                         if isa(tmp,'double')
-                            value(i) = tmp ;
+                            try
+                                value(i,:) = tmp ;
+                            catch
+                                a = size(value,2);
+                                b = size(tmp,2);
+                                if a > b
+                                    value(i,:) =[tmp,NaN(1,a-b)] ;
+                                elseif a < b
+                                    value(i,:) = tmp(1:size(value,2)) ;
+                                else
+                                    error('unknwon case...')
+                                end
+
+                                
+                            end
                         elseif isa(tmp,'Signal') || isa(tmp,'struct') ||  isa(tmp,'Exfo_laser') ||  isa(tmp,'DC_supply')
                             if i == 1
                                 value = {};

Classes/Warehouse_class/classes/Parameter.m

@@ -0,0 +1,7 @@
+classdef Parameter < StorageParameter
+    methods
+        function obj = Parameter(varargin)
+            obj@StorageParameter(varargin{:});
+        end
+    end
+end

Classes/Warehouse_class/functions/phase_predist_plots/fwm_plots/automate_JLT_plots.m

@@ -5,27 +5,37 @@
 wh = load([path filesep file]);
 wh = wh.wh;
 
+% fields = fieldnames(wh.parameter);
+% for k = 1:numel(fields)
+%     oldParam = wh.parameter.(fields{k});
+%     % copy over the properties to your new class
+%     wh.parameter.(fields{k}) = StorageParameter(...
+%         oldParam.Name, oldParam.values);
+% end
+% 
+
+
 plotJob = struct();
 width = 350;
 height = 200;
 plotJob.Position = [100 100 width 100+height];
 cols = cbrewer2("paired",12);
 plotJob.color = cols(1,:);
-plotJob.l = 2;
+plotJob.l = 10;
 plotJob.ch = 16;
 plotJob.d = 0;
 plotJob.sgm = 0;
 plotJob.pol = "copolarized";
 plotJob.p_in = 3;
-plotJob.gamma = 0;
-plotJob.pmd = 0;
+plotJob.gamma = 0.0023;
+plotJob.pmd = 0.1;
 plotJob.channelspacing = 400e9;
-plotJob.randzdw = 0;
+plotJob.randzdw = 1;
 
 
-plotJob.plot_ber_curve = 1;
+plotJob.plot_ber_curve = 0;
 plotJob.plot_3dber_curve = 0;
-plotJob.plot_violin = 0;
+plotJob.plot_violin = 1;
 plotJob.plot_wavelength_sweep = 0;
 plotJob.plot_wavelength_sweep_failure_rate = 0;
 
@@ -34,13 +44,20 @@ plotJob.dataStatArg = 'Lineplot with quartiles';
 plotJob.plotTypeArg = 'Lines';
 plotJob.displayname = 'a';
 plotJob.title = 'title';
-plotJob.figName = '16 Chann__';
+plotJob.figName = '16 Chann';
 plotJob.xAxisLabel = 'ROP per Channel in dBm';
 plotJob.yAxisLabel = 'BER';
 
-% createbercurves(wh,plotJob)
-createviolinplots(wh,plotJob);
-% createsweepplots(wh,plotJob);
+%%
+
+ % createbercurves(wh,plotJob)
+
+ P = [3];
+ for i = 1:2
+     plotJob.p_in = P(i); 
+     createviolinplots(wh,plotJob);
+ end
+ % createsweepplots(wh,plotJob);
 
 
 %% 1
@@ -64,7 +81,7 @@ D = [0,0,0,3,0,0,0,3];
 Sgm = [0,0,0,1,0,0,0,1];
 
 colidx = [4,8,6];
-P_launch = [0,3,6];
+P_launch = [3,6];
 
 fig = figure('Name',plotJob.figName);
 fig.Position = plotJob.Position;
@@ -81,7 +98,7 @@ for idx = 1:(numRows * numCols)
     plotJob.sgm = Sgm(idx);
     plotJob.randzdw = 1;
 
-    for i = 1:3
+    for i = 1:length(P_launch)
 
         plotJob.p_in = P_launch(i);
         plotJob.color = cols(colidx(i),:);
@@ -199,6 +216,7 @@ end
 
 %% 2
 function createviolinplots(wh,plotJob)
+
 width = 350;
 height = 200;
 s = 100;
@@ -208,30 +226,34 @@ cols = cbrewer2("paired",12);
 numRows = 1;
 numCols = 4;
 
-
 plotJob.ch = 16;
-plotJob.p_in = 3;
-plotJob.randzdw = 0;
+plotJob.randzdw = 1;
 
 Pol = ["copolarized","copolarized","alternated","paired",];
 Title = ["Co Pol.","Link Segmentation","Paired Pol. Interl.","Alternating Pol. Interl."];
 D = [0,3,0,0];
 Sgm = [0,1,0,0];
 
-colidx = [2];
-Len = [2];
+colidx = [4];
+Len = plotJob.l;
 
-plotJob.figName = ['_Violin',num2str(plotJob.ch),' Channels; ',num2str(plotJob.channelspacing*1e-9),' GHz; ',num2str(plotJob.p_in),' dBm; randomized: ', num2str(plotJob.randzdw)];
-fig = figure('Name',plotJob.figName);
-fig.Position = plotJob.Position;
-fig.Units = "centimeters";
-fig.Position = [0 0 18 7];
+fig = findall(groot, 'Type', 'figure', 'Name', plotJob.figName);
+if isvalid(fig)
+    figure(fig)
+    % fig = get(fig);
+    AxesMain =  fig.CurrentAxes;
+    hold on
+    % t = tiledlayout(1,4,'TileSpacing','compact','Padding','compact');
+else
+    fig = figure('name',char(plotJob.figName));
+    AxesMain    = gca;
+    hold on; grid on;
+    % t = tiledlayout(1,4,'TileSpacing','compact','Padding','compact');
+end
 
-t = tiledlayout(numRows,numCols,'TileSpacing','compact','Padding','compact');
 for idx = 1:(numRows * numCols)
     % Create subplot
-    %subplot(numRows, numCols, idx);
-    nexttile;
+    subplot(numRows, numCols, idx);
 
     plotJob.pol = Pol(idx);
     plotJob.d = D(idx);
@@ -295,7 +317,6 @@ annotation(fig,'textbox',...
     'FontSize',8,...
     'FitBoxToText','off');
 
-copygraphics(t,'BackgroundColor','none');
 %     lgd = legend('$P_{\mathrm{in}}=0$ dBm','$P_{\mathrm{in}}=3$ dBm','$P_{\mathrm{in}}=6$ dBm','Interpreter','latex');
 %     lgd.NumColumns = 3;
 %     lgd.Layout.Tile = 'south';

Classes/Warehouse_class/functions/phase_predist_plots/fwm_plots/automate_PTL_plot_new.m

@@ -0,0 +1,137 @@
+% Select dataset
+[file, path] = uigetfile("C:\Users\Silas\Documents\MATLAB\Datensätze\Raw_Cluster_Simulations\session_februar_24\wh_mi_nacht.mat");
+wh = load(fullfile(path, file));
+wh = wh.wh;
+
+%% --- Plot Settings ---
+cols = cbrewer2("Paired", 12);
+
+plotJob = struct();
+plotJob.Position = [100 100 600 400];
+plotJob.channelspacing = 400e9;
+plotJob.ch = 16;
+plotJob.d = 0;
+plotJob.sgm = 0;
+plotJob.gamma = 0.0023;
+plotJob.pmd = 0.1;
+plotJob.randzdw = 1;
+plotJob.plot_ber_curve = 1;
+plotJob.xAxisLabel = 'ROP per $\lambda$ [dBm]';
+plotJob.yAxisLabel = 'BER';
+plotJob.figName = 'avg  BER_vs_Plaunch_combined';
+plotJob.dataStatArg = 'Lineplot with quartiles';%'All Channels; mean(PMD Realizations)';Lineplot with quartiles
+plotJob.plotTypeArg = 'Lines';
+plotJob.displayname = 'bla';
+% --- Parameter combinations ---
+Len = [2, 10];
+Pol = ["copolarized", "copolarized", "alternated", "paired"];
+Title = ["CoPol","LS", "API", "PPI"];
+D = [0, 3, 0, 0];
+Sgm = [0, 1, 0, 0];
+% Pol = ["copolarized", "copolarized"];
+% Title = ["CoPol","LS"];
+% D = [0, 3];
+% Sgm = [0, 1];
+colidx = [6,4,2,2]; % color indices for different schemes
+
+P_launch = [0,3,6]; % input power sweep
+
+%% --- Create Figure ---
+
+fig = findall(groot, 'Type', 'figure', 'Name', plotJob.figName);
+
+if isvalid(fig)
+    figure(fig)
+    % fig = get(fig);
+    AxesMain =  fig.CurrentAxes;
+    hold on
+    % t = tiledlayout(1,4,'TileSpacing','compact','Padding','compact');
+else
+    fig = figure('name',char(plotJob.figName));
+    AxesMain    = gca;
+    hold on; grid on;
+    % t = tiledlayout(1,4,'TileSpacing','compact','Padding','compact');
+end
+
+dsa = ["AVG"];
+
+for d = 1
+
+    plotJob.dataStatArg = dsa(d);
+    cnt = 1;
+
+    for l = 1:numel(Len)
+
+        subplot(1,2,cnt);
+        cnt = cnt+1;
+    
+        for s = 1:length(P_launch)
+    
+    
+            for p = 1:numel(Title)
+    
+                plotJob.l = Len(l);
+    
+                plotJob.pol = Pol(p);
+                plotJob.d = D(p);
+                plotJob.sgm = Sgm(p);
+    
+                % color + style per length
+                baseColor = cols(colidx(p), :);
+                
+                if s == 1
+                    plotJob.linestyle = '-';
+                elseif s == 2
+                    plotJob.linestyle = '--';
+                else
+                    plotJob.linestyle = ':';
+                end
+    
+
+
+                if p == 1
+                    % plotJob.linestyle = '-';
+                    plotJob.markerstyle = 'o';
+                    plotJob.markersize = 2;
+                elseif p == 2
+                    % plotJob.linestyle = ':';
+                    plotJob.markerstyle = 'square';
+                    plotJob.markersize = 2;
+                elseif p == 3
+                    % plotJob.linestyle = '-';
+                    plotJob.markerstyle = 'x';
+                    plotJob.markersize = 6;
+                else
+                    % plotJob.linestyle = '-';
+                    plotJob.markerstyle = 'diamond';
+                    plotJob.markersize = 2;
+                end
+
+                % if d == 1
+                %     plotJob.linestyle = '-';
+                % else
+                %     plotJob.linestyle = ':';
+                %     plotJob.markerstyle = 'none';
+                % end
+    
+                plotJob.p_in = P_launch(s);
+    
+                plotJob.displayname = sprintf('%s',Title(p));
+                plotJob.color = baseColor;% * (1 - 0.15*(p-1)); % slight shade for powers
+                plotCurve(wh, plotJob);
+                % h = findobj(gca,'Type','Line','-not','Tag','FEC');
+                % set(h(p),'DisplayName',sprintf('%s (%.0f km, %.0f dBm)',Title(p),Len(l),P_launch(s)));
+                % title(sprintf('%d km; %d Channels, \Delta f = %.0f GHz', ...
+                %     plotJob.l, plotJob.ch, plotJob.channelspacing*1e-9));
+            end
+        end
+    end
+end
+set(gca, 'YScale', 'log');
+xlabel(plotJob.xAxisLabel);
+ylabel(plotJob.yAxisLabel);
+
+% legend('Interpreter','latex','NumColumns',2,'Location','southoutside');
+grid on; box on;
+
+copygraphics(fig, 'BackgroundColor','none');

Classes/Warehouse_class/functions/phase_predist_plots/fwm_plots/plotCurve.m

@@ -6,7 +6,7 @@ fig = findall(groot, 'Type', 'figure', 'Name', plotJob.figName);
 
 if isvalid(fig)
     figure(fig)
-    fig = get(fig);
+    % fig = get(fig);
     AxesMain =  fig.CurrentAxes;
     hold on
 else
@@ -28,32 +28,39 @@ realization = wh.parameter.realization.values(1:end);
 % get all xAxis values
 xAxis = wh.parameter.p_out.values;
 
+markerstyle = 'o';
+linestyle = '-';
+
 % Fetch Data from Warehouse
 for xl = 1:numel(xAxis)
     p_out = xAxis(xl);
     if string(plotJob.dataStatArg) == "Worst"
         temp = wh.getStoValue('ber',plotJob.l,plotJob.d,plotJob.sgm,string(plotJob.pol),plotJob.p_in,p_out,plotJob.pmd,plotJob.gamma,realization,plotJob.ch,1310,plotJob.channelspacing,plotJob.randzdw);
         temp = removeZeros(temp);
-        ber(xl) = max(temp,[],'all');
+        ber(xl) = quantile(temp,0.9,"all");
+        % ber(xl) = max(temp,[],'all');
         linew = 1.0;
-        markersz = 3;
-        linestyle = '-';
+        markersz = plotJob.markersize;
+        markerstyle =  plotJob.markerstyle;
+        linestyle = plotJob.linestyle;
         
     elseif string(plotJob.dataStatArg) == "AVG"
         temp = wh.getStoValue('ber',plotJob.l,plotJob.d,plotJob.sgm,string(plotJob.pol),plotJob.p_in,p_out,plotJob.pmd,plotJob.gamma,realization,plotJob.ch,1310,plotJob.channelspacing,plotJob.randzdw);
         temp = removeZeros(temp);
         ber(xl) = mean(temp,'all');
         linew = 1.0;
-        markersz = 3;
-        linestyle = '--';
+        markersz = plotJob.markersize;
+        markerstyle =  plotJob.markerstyle;
+        linestyle =  plotJob.linestyle;
 
     elseif string(plotJob.dataStatArg) == "Best"
         temp = wh.getStoValue('ber',plotJob.l,plotJob.d,plotJob.sgm,string(plotJob.pol),plotJob.p_in,p_out,plotJob.pmd,plotJob.gamma,realization,plotJob.ch,1310,plotJob.channelspacing,plotJob.randzdw);
         temp = removeZeros(temp);
         ber(xl) = min(temp,[],'all');
         linew = 1.0;
-        markersz = 3;
-        linestyle = '-';
+        markersz = plotJob.markersize;
+        markerstyle =  plotJob.markerstyle;
+        linestyle =  plotJob.linestyle;
 
     elseif string(plotJob.dataStatArg) == "All Channels; mean(PMD Realizations)"
         temp = wh.getStoValue('ber',plotJob.l,plotJob.d,plotJob.sgm,string(plotJob.pol),plotJob.p_in,p_out,plotJob.pmd,plotJob.gamma,realization,plotJob.ch,1310,plotJob.channelspacing,plotJob.randzdw);
@@ -63,7 +70,8 @@ for xl = 1:numel(xAxis)
 
         linew = 1;
         markersz = 1;
-        linestyle = '-';
+        markerstyle =  plotJob.markerstyle;
+        linestyle = plotJob.linestyle;
 
     elseif string(plotJob.dataStatArg) == "Lineplot with quartiles"
 
@@ -75,14 +83,16 @@ for xl = 1:numel(xAxis)
         upperq(xl) = quantile(temp,0.99,"all");
         lowerq(xl) = quantile(temp,0.04,"all");
         
-        if lowerq(xl) == 0
-            lowerq(xl) = lowerq(xl-1);
-        end
+
         %         upperq(xl) =  0.5*std(tmp,1,'all','omitnan');
         %         lowerq(xl) =  0.5*std(tmp,1,'all','omitnan');
 
-        %         upperq(xl) = max(dataNoNans);
-        %         lowerq(xl) = min(dataNoNans);
+        upperq(xl) = max(temp(:));
+        lowerq(xl) = min(temp(:));
+
+        if lowerq(xl) == 0
+            lowerq(xl) = 1e-8;
+        end
 
         %                 upperq(xl) = mean(tmp,"all","omitnan") + 1.96 * (std(tmp,1,'all','omitnan')/sqrt(numel(tmp)));
         %                 lowerq(xl) = mean(tmp,"all","omitnan") - 1.96 * (std(tmp,1,'all','omitnan')/sqrt(numel(tmp)));
@@ -149,6 +159,7 @@ elseif string(plotJob.plotTypeArg) == "Lines"
     %             [xAxis,ber] = interpCurve(xAxis, ber);
     %         end
 
+    cols = cbrewer2('RdBu',size(ber,1));
     for rlz = 1:size(ber,1)
 % 
         if (string(plotJob.dataStatArg) == "All Channels ;All PMD Realizations")||(string(plotJob.dataStatArg) == "All Channels; mean(PMD Realizations)")
@@ -160,6 +171,7 @@ elseif string(plotJob.plotTypeArg) == "Lines"
 
         if rlz < size(ber,1)
             
+            col = cols(rlz,:);
 
             s = plot(xAxis,ber(rlz,:),linestyle,'Marker',"none",'MarkerSize',markersz,'LineWidth',linew,'Color',col,'Parent',  AxesMain,'HandleVisibility','off');
 
@@ -169,22 +181,25 @@ elseif string(plotJob.plotTypeArg) == "Lines"
             s.DataTipTemplate.DataTipRows(1);
             s.DataTipTemplate.DataTipRows(2) = [];
 
-
         else
 
             if string(plotJob.dataStatArg) == "Lineplot with quartiles"
-                [hl,hp] = boundedline(xAxis,ber(rlz,:),([(ber(rlz,:)-lowerq(rlz,:));upperq(rlz,:)-ber(rlz,:)]'),'-o','alpha','Color',col,'transparency', 0.1,'linewidth',0.7);
+
+                [hl,hp] = boundedline(xAxis,ber(rlz,:),([(ber(rlz,:)-lowerq(rlz,:));upperq(rlz,:)-ber(rlz,:)]'),'-o','alpha','Color',col,'transparency', 0.06,'linewidth',0.7);
                 hl.MarkerFaceColor = col;
-                hl.MarkerSize = 3;
+                hl.MarkerSize = 2;
                 set(hp,'HandleVisibility','off');
                 %hp.LineWidth = 1.2;
 
                 ho = outlinebounds(hl,hp);
-                set(ho, 'linestyle', ':', 'color', col,'Linewidth',0.5);
+                set(ho, 'linestyle', ':', 'color', col,'Linewidth',0.6);
                 set(ho,'HandleVisibility','off');
+
+                % errorbar(xAxis,ber(rlz,:),ber(rlz,:)-lowerq(rlz,:),upperq(rlz,:)-ber(rlz,:),'-o','Color',col,'linewidth',0.7);
+
             else
 
-                s = plot(xAxis,ber(rlz,:),linestyle,'Marker',"o",'MarkerFaceColor',col,'MarkerSize',markersz,'LineWidth',linew,'Color',col,'Parent',  AxesMain,'DisplayName',[plotJob.displayname]);
+                s = plot(xAxis,ber(rlz,:),linestyle,'Marker',markerstyle,'MarkerFaceColor',col,'MarkerSize',markersz,'LineWidth',linew,'Color',col,'Parent',  AxesMain,'DisplayName',[plotJob.displayname]);
                 
                 s.DataTipTemplate.Interpreter = "latex";
                 s.DataTipTemplate.DataTipRows(1).Label = "Ch: ";
@@ -245,9 +260,10 @@ grid minor
 fontsize(AxesMain,8,"points")
 % fontname(AxesMain,"Arial")
 
-fig.Position = plotJob.Position;
-fig.Units = "centimeters";
-fig.Position = [2 2 8.5 7];
+
+% fig.Position = plotJob.Position;
+% fig.Units = "centimeters";
+% fig.Position = [2 2 8.5 7];
 
 set(AxesMain,'TickLabelInterpreter','latex')
 
@@ -257,7 +273,7 @@ set(AxesMain.Legend,'Interpreter','latex')
 
 ylim([1e-5,0.3]);
 
-xlim([min(xAxis),-3]);
+xlim([-10,-4]);
 
 
 % annotation('textbox', [0.125, 0.32, 0.1, 0.1], 'String', "FEC 3.8e-3","LineStyle","none","FontSize",8,"FontUnits","points")

Classes/Warehouse_class/functions/phase_predist_plots/fwm_plots/plotViolin.m

@@ -155,7 +155,7 @@ ylabel('Penalty in dB');
 xlabel('Channel Number');
 ylim([-9.3,-3]);
 xlim([0,plotJob.ch+1]);
-grid minor;
+% grid minor;
 set(gca, 'color', 'none');
 legend = [];
 

Datatypes/clr.m

@@ -49,22 +49,5 @@ classdef clr
             hold off;
         end
 
-        function showSet(colorStruct)
-            % Show colors from a structure in a bar plot
-            names = fieldnames(colorStruct);
-            colors = cell2mat(struct2cell(colorStruct)');
-
-            figure;
-            hold on;
-            for i = 1:size(colors, 1)
-                fill([0 1 1 0], [i-1 i-1 i i], colors(i, :), 'EdgeColor', 'k');
-                text(1.1, i-0.5, names{i}, 'FontSize', 12, 'Interpreter', 'none');
-            end
-            ylim([0, size(colors, 1)]);
-            xlim([0, 1.5]);
-            axis off;
-            title('Color Preview');
-            hold off;
-        end
     end
 end

Datatypes/equalizer_structure.m

@@ -8,6 +8,7 @@ classdef equalizer_structure < int32
         % db_precoded     (3)
         vnle_db_mlse    (4)
         db_encoded 	    (5)
+        ml_mlse         (6)
     end
 
 end

Functions/EQ_structures/dsp_runid.m

@@ -13,14 +13,13 @@ arguments
 end
 
 try
-
-
     % Initialize output structures
     output.ffe_package = {};
     output.mlse_package = {};
     output.vnle_package = {};
     output.dbtgt_package = {};
     output.dbenc_package = {};
+    output.mlmlse_package = {};
 
     if options.mode == "load_run_id" || options.append_to_db
         % Initialize database connection
@@ -96,11 +95,12 @@ try
     adaption= 1;
     use_dd_mode = 1;
 
-    use_ffe         = 1;
-    use_dfe         = 1;
-    use_vnle_mlse   = 1;
-    use_dbtgt       = 1;
+    use_ffe         = 0;
+    use_dfe         = 0;
+    use_vnle_mlse   = 0;
+    use_dbtgt       = 0;
     use_dbenc       = 0;
+    use_ml_mlse     = 1;
 
     addProcessingResultToDatabase = 0;
 
@@ -130,7 +130,7 @@ try
         mlse_  = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
         
         eq_post = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",2001,"sps",1,"decide",0,"adaption_technique","lms");
-        eq_post =FFE("epochs_tr",5,"epochs_dd",2,"len_tr",2^13,"mu_dd",mu_dd,"mu_tr",mu_tr,"order",25,"sps",2,"decide",0, "adaption",adaption_method(adaption),"dd_mode",use_dd_mode);
+        eq_post = FFE("epochs_tr",5,"epochs_dd",2,"len_tr",2^13,"mu_dd",mu_dd,"mu_tr",mu_tr,"order",25,"sps",2,"decide",0, "adaption",adaption_method(adaption),"dd_mode",use_dd_mode);
         % Duobinary signaling (db encoded)
         mlse_db_enc = MLSE("DIR", [1,1], "duobinary_output", 0, "M", M, "trellis_states", PAMmapper(M,0).levels);
         eq_db_enc = EQ("Ne", ffe_order, "Nb", dfe_order, "training_length", len_tr, ...
@@ -140,7 +140,12 @@ try
         % Preprocess signal
         Scpe_sig = preprocessSignal(Scpe_cell{r}, Symbols, fsym);
 
-        % Scpe_sig.spectrum("fignum",2223,"normalizeTo0dB",1,"displayname",'Rx');
+        Scpe_sig.spectrum("fignum",200,"normalizeTo0dB",1,"displayname",'Rx','addDCoffset',-6);
+
+        Scpe_sig.spectrum("fignum",201,"normalizeTo0dB",0,"displayname",'Rx');
+
+        ylim([-30,3]);
+    	xlim([-5,100]);
         % Scpe_sig.spectrum("fignum",22233,"normalizeTo0dB",0,"displayname",'Rx');
         % Scpe_sig.eye(fsym,M,"fignum",1024);
 
@@ -194,14 +199,26 @@ try
 
                 pf_ncoeffs  = 1;
                 ffe_order   = [50, 5, 5];
-                eq_    = EQ("Ne",ffe_order,"Nb",dfe_order,"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
+                eq_    = EQ("Ne",ffe_order,"Nb",dfe_order,"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",0);
+                % eq_ = VNLE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",[0.0004 0.0005 0.0006],"mu_tr",0.0004,"order",[50,5,5],"sps",2,"decide",0);
                 pf_    = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
 
                 useviterbi = 0;
                 if useviterbi
                     mlse_  = MLSE_viterbi("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
                 else
-                    mlse_  = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
+                    
+                    if duob_mode == db_mode.no_db && M == 6 %only for PAM-6 and no duobinary precoding, otherwise leads to false sequence estimation
+                        trellexlusion = 1;
+                    else
+                        trellexlusion = 0;
+                    end
+                    
+                    %state_mode 3 -> stat lvl; state_mode 2 -> use target lvls
+                    %scale_mode 2 -> mmse adaption
+
+                    mlse_  = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels,'scale_mode',2,'trellis_exclusion',trellexlusion,'trellis_state_mode',2);
+
                 end
 
                 [ffe_results, mlse_results] = vnle_postfilter_mlse(eq_, pf_, mlse_, M, Scpe_sig, Symbols, Tx_bits, ...
@@ -222,38 +239,38 @@ try
                     database.addProcessingResult(run_id, ffe_results.metrics, ffe_results.config);
                 end
 
-                % pf_ncoeffs  = 2;
-                % eq_    = EQ("Ne",ffe_order,"Nb",dfe_order,"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
-                % pf_    = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
-                % % mlse_  = MLSE_viterbi("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
-                % mlse_  = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
-                % 
-                % [ffe_results, mlse_results] = vnle_postfilter_mlse(eq_, pf_, mlse_, M, Scpe_sig, Symbols, Tx_bits, ...
-                %     "precode_mode", duob_mode,...
-                %     'showAnalysis', 0, ...
-                %     "postFFE", [],...
-                %     "eth_style_symbol_mapping", 0);
-                % 
-                % ffe_results.metrics.print;
-                % mlse_results.metrics.print;
-                % 
-                % output.mlse_package{r} = mlse_results;
-                % output.vnle_package{r} = ffe_results;
-                % 
-                % if options.append_to_db
-                %     database.addProcessingResult(run_id, mlse_results.metrics, mlse_results.config);
-                %     database.addProcessingResult(run_id, ffe_results.metrics, ffe_results.config);
-                % end
+            end
+
+            if use_ml_mlse
+
+                %ML-based MLSE (L=2)
+                mu_ml = 0.01; training_epochs = 100;
+                ml_mlse_equalizer = ML_MLSE("epochs_tr",training_epochs,"epochs_dd",1, ...
+                    "len_tr",length(Scpe_sig),"mu_dd",mu_ml,"mu_tr",mu_ml,"order",11,"sps",2, ...
+                    "traceback_depth",128,"L",1,"delta",4,"adaptive_mu",0);
+
+                [ml_mlse_results] = ml_mlse(ml_mlse_equalizer, M, Scpe_sig, Symbols, Tx_bits,"precode_mode",duob_mode);
+                output.mlmlse_package{r} = ml_mlse_results;
+           
+                if options.append_to_db
+                    database.addProcessingResult(run_id, ml_mlse_results.metrics, ml_mlse_results.config);
+                end
 
             end
             
+
             if use_dbtgt
 
                 useviterbi = 0;
                 if useviterbi
                     mlse_db_  = MLSE_viterbi("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
                 else
-                     mlse_db_ = MLSE("DIR",[1,1],"duobinary_output",0,"M",M,"trellis_states",PAMmapper(M,0).levels);
+                    if duob_mode == db_mode.no_db && M == 6 %only for PAM-6 and no duobinary precoding, otherwise leads to false sequence estimation
+                        trellexlusion = 1;
+                    else
+                        trellexlusion = 0;
+                    end
+                     mlse_db_ = MLSE("DIR",[1,1],"duobinary_output",0,"M",M,"trellis_states",PAMmapper(M,0).levels,'scale_mode',2,'trellis_exclusion',trellexlusion,'trellis_state_mode',3);
                 end
                 ffe_order   = [50, 5, 5];
                 eq_    = EQ("Ne",ffe_order,"Nb",dfe_order,"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
@@ -276,6 +293,9 @@ try
 
         if duob_mode == db_mode.db_encoded
 
+            mlse_db_enc  = MLSE_viterbi("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
+            mlse_db_enc = MLSE("DIR", [1,1], "duobinary_output", 0, "M", M, "trellis_states", PAMmapper(M,0).levels);
+
             db_results = duobinary_signaling(eq_db_enc, mlse_db_enc, M, Scpe_sig, Symbols, Tx_bits, "precode_mode",duob_mode, "showAnalysis",0,"postFFE",[]);
             output.dbenc_package{r} = db_results;
             if options.append_to_db

Functions/EQ_structures/duobinary_signaling.m

@@ -35,9 +35,28 @@ if ~isempty(options.postFFE)
     [eq_signal, eq_noise] = options.postFFE.process(eq_signal, tx_symbols);
 end
 
-% Process through MLSE
-% [mlse_signal] = mlse_.process(eq_signal);
-[mlse_signal,~,GMI_MLSE] = mlse_.process(eq_signal,tx_symbols);
+if isa(mlse_,'MLSE_viterbi')
+     [mlse_signal] = mlse_.process(eq_signal);
+else
+
+    % Aufpassen mit welcher Sequenz man hier vergleicht für LLR stuff...
+    % gespeichtere "Symbols" sind schon DB codiert, das wollen wir hier
+    % nicht! Sondern die precoded aber nicht db-encoded müssen als ref in
+    % die LLR berechnung gehen! 
+    ref_sym = PAMmapper(M,0).map(tx_bits); %ist klar
+    ref_sym_dpc = Duobinary().precode(ref_sym); % precoded 
+    % ref_sym_dbenc = Duobinary().encode(ref_sym_dpc); %encoded - das wurde gesendet!
+    % ref_sym_dec = Duobinary().decode(ref_sym_dbenc); %ref_sym wieder zurück!
+
+    mlse_.trellis_states = PAMmapper(M,0).levels;
+    mlse_.trellis_state_mode = 1;
+    [mlse_signal,LLR,GMI_MLSE] = mlse_.process(eq_signal,ref_sym_dpc);
+end
+
+
+
+% tx_symbols_ = Duobinary().decode(tx_symbols);
+% [mlse_signal,~,GMI_MLSE] = mlse_.process(eq_signal,tx_symbols);
 
 % Apply duobinary encoding and decoding
 mlse_signal = Duobinary().encode(mlse_signal);

Functions/EQ_structures/duobinary_target.m

@@ -130,6 +130,10 @@ db_results.config.equalizer_structure = int32(equalizer_structure.vnle_db_mlse);
 db_results.config.comment = 'function: Duobinary tgt. (VNLE -> MLSE)';
 
 if options.showAnalysis
+
+    eq_signal.eye(eq_signal.fs,M,"fignum",249);
+
+
     eq_noise = eq_noise - mean(eq_noise.signal);
 
     rx_signal.spectrum("normalizeTo0dB",1,"fignum",250,"displayname","Rx Spectrum");

Functions/EQ_structures/ml_mlse.m

@@ -0,0 +1,140 @@
+function [ml_mlse_results] = ml_mlse(eq_, M, rx_signal, tx_symbols, tx_bits, options)
+% 
+%
+% Inputs:
+%   eq_ - Equalizer object
+%   M - Modulation order
+%   rx_signal - Received signal
+%   tx_symbols - Transmitted symbols
+%   tx_bits - Transmitted bits
+%   options - Optional parameters
+%
+% Outputs:
+%   ffe_results - Results from FFE processing
+
+arguments
+    eq_
+    M
+    rx_signal
+    tx_symbols
+    tx_bits
+    options.precode_mode db_mode
+    options.eth_style_symbol_mapping = 0;
+    options.postFFE = [];
+
+end
+
+%% Process signals through equalizer
+
+[eq_signal_hd,y_ref] = eq_.process(rx_signal,tx_symbols);
+
+%% Calculate BER based on precoding mode
+[bits, errors, ber, error_pos, errors_precoded, ber_precoded] = calculateBER(eq_signal_hd, tx_symbols, tx_bits, options.precode_mode, M, options.eth_style_symbol_mapping);
+
+
+% Create FFE results structure
+ml_mlse_results = struct();
+try
+    eq_.e = [];
+    eq_.e2 = [];
+    eq_.e3 = [];
+    eq_.b = [];
+    eq_.b2 = [];
+    eq_.b3 = [];
+end
+
+ml_mlse_results.config = Equalizerstruct();
+
+eq_small = strip_eq(eq_, 10);
+json_str = jsonencode(eq_small);
+
+ml_mlse_results.config.eq = jsonencode(eq_);
+ml_mlse_results.config.equalizer_structure = int32(equalizer_structure.ml_mlse);
+ml_mlse_results.config.comment = 'function: ML-based MLSE';
+
+ml_mlse_results.metrics = Metricstruct;
+% ml_mlse_results.metrics.result_id = NaN;
+% ml_mlse_results.metrics.run_id = NaN;
+% ml_mlse_results.metrics.eqParam_id = NaN;
+ml_mlse_results.metrics.date_of_processing = datetime('now');
+ml_mlse_results.metrics.BER = ber;
+ml_mlse_results.metrics.numBits = bits;
+ml_mlse_results.metrics.numBitErr = errors;
+ml_mlse_results.metrics.BER_precoded = ber_precoded;
+ml_mlse_results.metrics.numBitErr_precoded = errors_precoded;
+% ml_mlse_results.metrics.SNR = NaN;
+% ml_mlse_results.metrics.SNR_level = NaN;
+% ml_mlse_results.metrics.STD = NaN;
+% ml_mlse_results.metrics.STD_level = NaN;
+% ml_mlse_results.metrics.STDrx = NaN;
+% ml_mlse_results.metrics.STDrx_level = NaN;
+% ml_mlse_results.metrics.GMI = NaN;
+% ml_mlse_results.metrics.AIR = NaN;
+% ml_mlse_results.metrics.EVM = NaN;
+% ml_mlse_results.metrics.EVM_level = NaN;
+% ml_mlse_results.metrics.Alpha = NaN;
+
+
+end
+
+%% Helper Functions
+function [bits, errors, ber, error_pos, errors_precoded, ber_precoded] = calculateBER(eq_signal_hd, tx_symbols, tx_bits, precode_mode, M, eth_style)
+% Calculate BER based on precoding mode
+mapper = PAMmapper(M, 0, "eth_style", eth_style);
+
+switch precode_mode
+    case db_mode.no_db
+        % TX Data is not precoded
+        % A) Emulate diff precoding
+        eq_signal_hd_precoded = Duobinary().encode(eq_signal_hd, "M", M);
+        eq_signal_hd_precoded = Duobinary().decode(eq_signal_hd_precoded, "M", M);
+        
+        tx_symbols_precoded = Duobinary().encode(tx_symbols);
+        tx_symbols_precoded = Duobinary().decode(tx_symbols_precoded);
+        
+        tx_bits_precoded = mapper.demap(tx_symbols_precoded);
+        
+        rx_bits = mapper.demap(eq_signal_hd_precoded);
+        [~, errors_precoded, ber_precoded, ~] = calc_ber(rx_bits.signal, tx_bits_precoded.signal, "skip_front", 30000, "skip_end", 150, "returnErrorLocation", 1);
+        
+        % B) Just determine BER
+        rx_bits = mapper.demap(eq_signal_hd);
+        [bits, errors, ber, error_pos] = calc_ber(rx_bits.signal, tx_bits.signal, "skip_front", 30000, "skip_end", 150, "returnErrorLocation", 1);
+        
+    case db_mode.db_precoded
+        % Data is precoded on TX side
+        % A) Decode at Rx if no DB targeting was applied
+        eq_signal_hd_decoded = Duobinary().encode(eq_signal_hd, "M", M);
+        eq_signal_hd_decoded = Duobinary().decode(eq_signal_hd_decoded, "M", M);
+        rx_bits_decoded = mapper.demap(eq_signal_hd_decoded);
+        [~, errors_precoded, ber_precoded, ~] = calc_ber(rx_bits_decoded.signal, tx_bits.signal, "skip_front", 30000, "skip_end", 150, "returnErrorLocation", 1);
+        
+        % B) Omit the Coding by comparing with demapped TX symbol sequence
+        tx_bits_demapped = mapper.demap(tx_symbols);
+        rx_bits = mapper.demap(eq_signal_hd);
+        [bits, errors, ber, error_pos] = calc_ber(rx_bits.signal, tx_bits_demapped.signal, "skip_front", 30000, "skip_end", 150, "returnErrorLocation", 1);
+end
+end
+
+
+function eq_out = strip_eq(eq_, max_elems)
+    % strip_eq removes all large fields from the ML_MLSE object
+    % eq_out = strip_eq(eq_, max_elems)
+    % max_elems ... maximum number of elements to keep (default = 10)
+
+    if nargin < 2
+        max_elems = 10; % default threshold
+    end
+
+    props = properties(eq_);
+    for i = 1:numel(props)
+        val = eq_.(props{i});
+        if ~isempty(val)
+            % Count total number of elements
+            if numel(val) > max_elems
+                eq_.(props{i}) = [];
+            end
+        end
+    end
+    eq_out = eq_;
+end

Functions/EQ_structures/vnle_postfilter_mlse.m

@@ -45,7 +45,26 @@ end
 eq_signal_hd = PAMmapper(M, 0).quantize(eq_signal_sd);
 
 % Process through postfilter and MLSE
+
 [mlse_sig_sd,whitened_noise] = pf_.process(eq_signal_sd, eq_noise);
+
+if 0 %tx_symbols.fs > 190e9
+    if pf_.ncoeff == 1
+            if pf_.coefficients(2) < 0
+                % coeff is negative for too high/ bad VNLE convergence
+                pf_.coefficients(2) = 0.9;
+       
+            end
+    else
+        %long memory / pf respinse - not sure what to set here in a worst
+        %case :-)
+
+    end
+    %do it again:
+    pf_.useBurg = 0;
+    [mlse_sig_sd,whitened_noise] = pf_.process(eq_signal_sd, eq_noise);
+end
+
 mlse_.DIR = pf_.coefficients;
 
 GMI_MLSE = NaN;
@@ -249,7 +268,8 @@ figure(336);
 hold on;
 eq_signal_sd.spectrum("displayname",'Equalized Signal','fignum',336,'normalizeTo0dB',0);
 eq_noise.spectrum("displayname",'Equalized Signal','fignum',336,'normalizeTo0dB',0);
-showEQNoisePSD(eq_noise, "fignum", 336, "displayname", 'Residual Noise after VNLE', 'postfilter_taps', pf_.coefficients);
+
+showEQNoisePSD(eq_noise, "fignum", 338, "displayname", 'Residual Noise after VNLE', 'postfilter_taps', pf_.coefficients);
 
 for t = 1:4
     pf_.ncoeff = t;
@@ -263,6 +283,7 @@ if ~isempty(postFFE)
     showEQcoefficients('n1', postFFE.e, "displayname", 'Coefficients', 'fignum', 338);
 end
 
+showEQcoefficients('n1', eq_.e,'n2', eq_.e2,'n3', eq_.e3, "displayname", 'Coefficients', 'fignum', 339);
 showEQfilter(eq_.e, eq_signal_sd.fs.*2);
 
 figure(340); clf;

Functions/EQ_visuals/showEQNoisePSD.m

@@ -22,7 +22,6 @@ end
 
     % Ensure the figure is ready before calling spectrum
     eq_noise.spectrum("displayname", options.displayname, "fignum", fig.Number, "normalizeTo0dB", 1,"color",options.color);
-    title('EEN')
 
     if ~isnan(options.postfilter_taps)
         % Hold on to the figure for further plotting
@@ -43,6 +42,6 @@ end
     end
 
     xlim([-eq_noise.fs/2* 1e-9 eq_noise.fs/2* 1e-9]);
-    ylim([-15, 0]);
+    % ylim([-15, 0]);
 
 end

Functions/EQ_visuals/showLevelHistogram.m

@@ -52,6 +52,8 @@ end
     legend
     grid on
 
+    % view([90 -90]);
+
 
 
 end

Functions/Job_Processing/configureEqualizers_remove.m

@@ -1,40 +0,0 @@
-function [eq_, pf_, mlse_, mlse_db_, eq_post] = configureEqualizers(M, len_tr, vnle_order, dfe_order, mu_dc, mu_ffe, mu_dfe, pf_ncoeffs)
-    % CONFIGUREEQUALIZERS Creates and configures equalizer objects
-    %
-    % Inputs:
-    %   M - PAM level
-    %   len_tr - Training length
-    %   vnle_order - Array with orders for VNLE [order1, order2, order3]
-    %   dfe_order - Array with orders for DFE
-    %   mu_dc - DC adaptation rate
-    %   mu_ffe - Array with adaptation rates for FFE [mu1, mu2, mu3]
-    %   mu_dfe - Adaptation rate for DFE
-    %   pf_ncoeffs - Number of coefficients for postfilter
-    %
-    % Outputs:
-    %   eq_ - Configured EQ object
-    %   pf_ - Configured Postfilter object
-    %   mlse_ - Configured MLSE_viterbi object
-    %   mlse_db_ - Configured MLSE_viterbi object for duobinary
-    %   eq_post - Configured FFE object for post-processing
-    
-    % Configure main equalizer
-    eq_ = EQ("Ne", vnle_order, "Nb", dfe_order, ...
-             "training_length", len_tr, "training_loops", 5, "dd_loops", 5, ...
-             "K", 2, "DCmu", mu_dc, "DDmu", [mu_ffe mu_dfe], ...
-             "DFEmu", 0.005, "FFEmu", 0, "plotfinal", 0, "ideal_dfe", 1);
-    
-    % Configure postfilter
-    pf_ = Postfilter("ncoeff", pf_ncoeffs, "useBurg", 1);
-    
-    % Configure MLSE objects
-    mlse_ = MLSE_viterbi("duobinary_output", 0, 'M', M, ...
-                         'trellis_states', PAMmapper(M,0).levels);
-    mlse_db_ = MLSE_viterbi("DIR", [1,1], "duobinary_output", 0, ...
-                            "M", M, "trellis_states", PAMmapper(M,0).levels);
-    
-    % Configure post-FFE
-    eq_post = FFE("epochs_tr", 5, "epochs_dd", 5, "len_tr", 4096*2, ...
-                  "mu_dd", 1e-4, "mu_tr", 0, "order", 2001, ...
-                  "sps", 1, "decide", 0);
-    end

Functions/Job_Processing/preprocessSignal.m

@@ -16,11 +16,17 @@ Scpe_sig = Scpe_sig.resample("fs_out", 2*fsym);
 [Scpe_sig, ~] = Scpe_sig.tsynch("reference", Symbols, "fs_ref", fsym, "debug_plots", 0);
 
 % Apply Gaussian filter
-% Scpe_sig = Filter('filtdegree', 4, "f_cutoff", Symbols.fs.*0.6, ...
-%                  "fs", Scpe_sig.fs, "filterType", filtertypes.gaussian, ...
-%                  "active", true).process(Scpe_sig);
+if 1
+    Scpe_sig = Filter('filtdegree', 8, "f_cutoff", Symbols.fs.*0.52, ...
+        "fs", Scpe_sig.fs, "filterType", filtertypes.gaussian, ...
+        "active", true).process(Scpe_sig);
+else
+    Scpe_sig = Filter('filtdegree', 4, "f_cutoff", Symbols.fs.*0.6, ...
+        "fs", Scpe_sig.fs, "filterType", filtertypes.gaussian, ...
+        "active", true).process(Scpe_sig);
+end
 
-% Remove DC offset
+%Remove DC offset
 Scpe_sig = Scpe_sig - mean(Scpe_sig.signal);
 
 end

Functions/Job_Processing/submitJobs.m

@@ -209,6 +209,7 @@ wh = submit_options.wh;
             wh.addValueToStorageByLinIdx(val.vnle_package, 'vnle_package', jobIndex);
             wh.addValueToStorageByLinIdx(val.dbtgt_package,'dbtgt_package',jobIndex);
             wh.addValueToStorageByLinIdx(val.dbenc_package,'dbenc_package',jobIndex);
+            wh.addValueToStorageByLinIdx(val.mlmlse_package,'mlmlse_package',jobIndex);
         end
     end
     function p = setupParallelPool(numWorkers, idleTimeout)

Functions/Lab_helper/loadFreqResp.m

@@ -1,5 +1,5 @@
 % Define the precomp path
-precomp_path = "C:\Users\sioe\Documents\MATLAB\imdd_simulation\projects\ECOC_2025\";
+precomp_path = "C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\precomp";
 
 % Step 1: Find all valid files (assume .mat files for ChannelFreqResp)
 fileList = dir(fullfile(precomp_path, '*.mat'));

Functions/Lab_helper/waitUntilClick.m

@@ -1,20 +1,50 @@
-function waitUntilClick()
-    % Create the UI figure
-    fig = uifigure('Name', 'Pause Execution', 'Position', [100 100 300 150]);
+function waitUntilClick(options)
 
-    % Create a label to inform the user
-    uilabel(fig, 'Position', [50 80 200 40], 'Text', 'Click "Continue" to proceed', ...
-            'FontSize', 14, 'HorizontalAlignment', 'center');
+% use like this to stop any computation and wait for user to press ENTER or
+% Continue button. I used this in the lab to wait until smth settled or I
+% set a device to a certain operating point...
 
-    % Create the "Continue" button
-    continueButton = uibutton(fig, 'push', 'Text', 'Continue', 'Position', [100 20 100 40], ...
-                              'ButtonPushedFcn', @(src, event) closeWindow());
+% USAGE:
+% waitUntilClick;
+
+% OPTIONAL: provide input text that is prompted :-)
+% usrcmd = sprintf('Laser to %d dBm',p);
+% waitUntilClick('Text',usrcmd);
+
+
+arguments
+    options.Text string = "Click ""Continue"" to proceed"
+end
+
+% Create the UI figure
+fig = uifigure('Name', 'Pause Execution', ...
+    'Position', [100 100 300 150], ...
+    'KeyPressFcn', @(src,event) keyHandler(event));
+
+% Create a label with custom text
+uilabel(fig, ...
+    'Position', [30 70 240 60], ...
+    'Text', options.Text, ...
+    'FontSize', 14, ...
+    'HorizontalAlignment', 'center', ...
+    'WordWrap','on');
+
+% Create the "Continue" button
+uibutton(fig, 'push', ...
+    'Text', 'Continue', ...
+    'Position', [100 20 100 40], ...
+    'ButtonPushedFcn', @(src, event) closeWindow());
 
-    % Function to close the window when "Continue" is clicked
     function closeWindow()
-        delete(fig); % Close the figure window
+        delete(fig);
     end
 
-    % Wait until the figure is closed to resume execution
-    waitfor(fig);
+    function keyHandler(event)
+        if strcmp(event.Key,'return') || strcmp(event.Key,'enter')
+            closeWindow();
+        end
+    end
+
+% Wait until the figure is closed
+waitfor(fig);
 end

Functions/Metrics/noiseFigureFromOSNR.m

@@ -0,0 +1,20 @@
+function NF_dB = noiseFigureFromOSNR(Pin_dBm, OSNR_dB, lambda_nm, Bref_nm)
+
+    if nargin < 4
+        Bref_nm = 0.1; % OSNR reference bandwidth
+    end
+
+    h = 6.62607015e-34;   % Planck [J*s]
+    c = 299792458;        % speed of light [m/s]
+
+    lambda = lambda_nm * 1e-9;
+    nu = c / lambda;
+
+    % convert reference bandwidth from nm to Hz
+    Bref_Hz = Bref_nm*1e-9 * c / lambda^2;
+
+    % ASE noise power density in dBm
+    Pn_dBm = 10*log10(h*nu*Bref_Hz) + 30;
+
+    NF_dB = Pin_dBm - OSNR_dB - Pn_dBm;
+end

Functions/Theory/CCDM/ccdm_gpt_example.m

@@ -0,0 +1,27 @@
+%% Target source entropy for PS-PAM8
+clear; clc;
+
+M = 8;
+a = -(M-1):2:(M-1);      % PAM-8 amplitude levels: [-7 -5 -3 -1 1 3 5 7]
+H_target = 2.79;          % desired entropy [bits/symbol]
+
+% Objective: find nu such that H(PA) = H_target
+f = @(nu) entropy_MB(a,nu) - H_target;
+nu_opt = fzero(f, [0, 2]);   % search ν in reasonable range
+
+% Compute final distribution
+P = exp(-nu_opt*a.^2);
+P = P/sum(P);
+H = -sum(P .* log2(P));
+
+fprintf('Shaping parameter ν = %.4f\n', nu_opt);
+fprintf('Entropy H(A) = %.3f bits/symbol\n', H);
+disp('Probability vector (P_A):');
+disp(P.');
+
+%% Helper: entropy function
+function H = entropy_MB(a,nu)
+    P = exp(-nu*a.^2);
+    P = P/sum(P);
+    H = -sum(P .* log2(P));
+end

Functions/Theory/Neuer Ordner/duobinary_histogram.m

@@ -1,51 +0,0 @@
-
-
-
-figure(2)
-tiledlayout(1,3)
-cols = linspecer(5);
-cnt = 1;
-for m = [4,6,8]
-
-    M = m;
-    fsym = 112e9;
-    fdac = 256e9;
-    
-    [Digi_sig,Symbols,Tx_bits] = PAMsource(...
-        "fsym",fsym,"M",M,"order",19,"useprbs",1,...
-        "fs_out",fdac,...
-        "applyclipping",0,"clipfactor",1.5,...
-        "applypulseform",0,"pulseformer",NaN,...
-        "randkey",33,...
-        "db_precode",1,"db_encode",0,...
-        "mrds_code",0,"mrds_blocklength",512).process();
-    
-    Symbols_pre = Duobinary().precode(Symbols);
-    
-    Symbols_db = Duobinary().encode(Symbols_pre);
-    
-    if M == 4
-        Symbols_db.signal = Symbols_db.signal .*sqrt(2.5);
-    elseif M == 6
-        Symbols_db.signal = Symbols_db.signal .*sqrt(5.8);
-    elseif M == 8
-        Symbols_db.signal = Symbols_db.signal .*sqrt(10.5);
-    end
-    
-    % figure(1)
-    % hold on
-    % histogram(Symbols_db.signal,"EdgeAlpha",0.3,"Normalization","probability");
-    
-    
-    % figure(1)
-    nexttile
-    hold on
-    bar(unique(Symbols_db.signal),histcounts(int32(Symbols_db.signal),"Normalization","probability"),"FaceColor",cols(cnt,:),"FaceAlpha",0.6,"BarWidth",1-(0.2*cnt),"LineWidth",0.5,"EdgeColor",'black','DisplayName',['Duobinary PAM-',num2str(M)]);
-    xticks(unique(Symbols_db.signal));
-    ylim([0 0.26]);
-    xlabel("Symbol")
-    
-
-    cnt = cnt+1,
-
-end

Functions/Theory/Neuer Ordner/duobinary_transferfunction.m

@@ -1,18 +0,0 @@
-
-
-% Define the filter taps
-h_ = {[1],[1 1],[1 2 1],[1 3 3 1]};
-
-for i = 1:length(h_)
-    h = h_{i};
-    
-    [H, w] = freqz(h, 1, 1024, 1);
-    
-    figure(1);
-    hold on
-    plot(w, 10*log10(abs(H)), 'LineWidth', 2,'DisplayName',['$(1+D)^2$']); %todo
-    xlabel('Normalized Frequency');
-    ylabel('Amplitude in dB');
-    grid on;
-    ylim([-20,10])
-end

Functions/Theory/dispersion_10km.m

@@ -0,0 +1,89 @@
+%% ============================================================
+%  IM/DD Fading Notch – λ_null vs. Bandwidth (Fixed 10 km)
+% ============================================================
+
+clear; clc;
+
+%% Fiber and dispersion parameters
+lambda0 = 1310e-9;   % Zero-dispersion wavelength [m]
+S0      = 0.09;       % Dispersion slope at ZDW [ps/(nm²·km)]
+L       = 10e3;      % Fiber length [m]
+c       = physconst('lightspeed');
+
+%% Frequency sweep (defines the desired first-fading notch)
+f_targets = linspace(40e9, 150e9, 200);   % [Hz]
+f_GHz = f_targets / 1e9;
+
+%% Compute wavelength λ_null for each target f_null
+[lambda_vec, Dacc_vec] = lambda_for_first_null_full(f_targets, L, lambda0, S0);
+lambda_nm = lambda_vec * 1e9;   % Convert to nm
+Dacc = Dacc_vec;                % [ps/nm]
+
+%% ------------------------------------------------------------
+% Plot λ_null vs. f_null for 10 km fiber
+% ------------------------------------------------------------
+cols = cbrewer2('Paired',10);
+figure('Color','w'); hold on;
+
+hLine = plot(lambda_nm, f_GHz, ...
+    'LineWidth', 2, ...
+    'DisplayName', sprintf('L = %.1f km', L/1000), ...
+    'Color', cols(2,:));
+
+xlabel('Wavelength  λ [nm]');
+ylabel('First fading notch  f_{null} [GHz]');
+title('IM/DD Fading Notch Position vs. Wavelength');
+grid on; box on;
+
+lim = (lambda0.*1e9) - [8, 40];
+xlim([lim(2) lim(1)]);
+yticks([56,75,90,112]);
+
+%% ------------------------------------------------------------
+% Custom DataTip Template
+% ------------------------------------------------------------
+% Add accumulated dispersion value to the DataTip
+hLine.DataTipTemplate.DataTipRows(1).Label = 'λ [nm]';
+hLine.DataTipTemplate.DataTipRows(2).Label = 'f_{null} [GHz]';
+
+% Create a new row for Dacc
+dRow = dataTipTextRow('D_{acc} [ps/nm]', Dacc);
+hLine.DataTipTemplate.DataTipRows(end+1) = dRow;
+
+%% ------------------------------------------------------------
+% Helper function: lambda_for_first_null_full
+% ------------------------------------------------------------
+function [lambda_vec, Dacc_vec] = lambda_for_first_null_full(f_target, L, lambda0, S0)
+    c = physconst('lightspeed');
+    S0_si = S0 * 1e3;   % ps/(nm²·km) -> s/(m³)
+
+    lambda_min = 1260e-9;
+    lambda_max = 1360e-9;
+
+    f_target = f_target(:);
+    N = numel(f_target);
+
+    lambda_vec = zeros(N,1);
+    Dacc_vec   = zeros(N,1);
+
+    for k = 1:N
+        RHS = c * 0.5 / (f_target(k)^2 * L);
+
+        fun = @(lambda) -(S0_si/4).*(lambda - (lambda0^4)./(lambda.^3)).*lambda.^2 - RHS;
+
+        try
+            lambda_sol = fzero(fun, [lambda_min, lambda0 * 0.999]);
+        catch
+            lambda_sol = lambda_min;
+        end
+
+        lambda_sol = min(max(lambda_sol, lambda_min), lambda_max);
+        lambda_vec(k) = lambda_sol;
+
+        D_lambda = (S0_si/4) * (lambda_sol - (lambda0^4)/(lambda_sol^3)) / 1e-6; % ps/(nm·km)
+        Dacc_val = D_lambda * (L/1000);                                          % ps/nm
+        Dacc_val = min(max(Dacc_val, -100), 100);
+
+        Dacc_vec(k) = Dacc_val;
+    end
+end

Functions/Theory/dispersion_contour.m

@@ -1,14 +1,15 @@
 % Gitter für lambda0 und S0
-lambda0_vec = linspace(1300,1320,200);
+lambda0_vec = linspace(1260,1360,200);
 S0_vec      = linspace(0.06,0.1,200);
 [Lambda0, S0] = meshgrid(lambda0_vec, S0_vec);
 
 % Festen Betriebsparameter
 lambda = 1293;  % nm
-L      = 10;    % km
+L      = 1;    % km
 
 % Dispersion berechnen (lineare Näherung)
 D = S0 .* ( lambda - Lambda0 ) * L;
+% D = (S0./4) .* ( lambda - (Lambda0.^4)./(lambda^3) ) * L;
 
 %% 2D-Konturplot nur mit Linien und Text
 figure('Color','w');

Functions/Theory/dispersion_contour_bandwidth_lambda.m

@@ -0,0 +1,146 @@
+%% ------------------------------------------------------------
+% Contour plot: λ_null as function of bandwidth (f_target) and reach (L)
+% ------------------------------------------------------------
+
+% Parameters
+lambda0 = 1310e-9;   % [m]
+S0      = 0.08;      % [ps/(nm²·km)]
+c       = physconst('lightspeed');
+
+% Sweep dimensions
+f_targets = linspace(50e9, 120e9, 100);   % [Hz]  (x-axis)
+L_values  = linspace(0.5e3, 10e3, 100);   % [m]   (y-axis)
+
+lambda_surface = zeros(numel(L_values), numel(f_targets));
+Dacc_surface   = zeros(numel(L_values), numel(f_targets));
+
+% Outer loop over fiber length (since L must be scalar)
+for iL = 1:numel(L_values)
+    L = L_values(iL);
+    [lambda_vec, Dacc_vec] = lambda_for_first_null_full(f_targets, L, lambda0, S0);
+    lambda_vec = 2*abs(lambda0 - lambda_vec);
+
+    if 0
+    fprintf('\n- %d km ------------------------------------\n',L);
+    fprintf('   f_null [GHz]     lambda [nm]     Dacc [ps/nm]\n');
+    fprintf('----------------------------------------------\n');
+    fprintf('%10.1f    %8.2f       %+8.3f\n',[f_targets(:)/1e9, lambda_vec(:)*1e9, Dacc_vec(:)].');
+    fprintf('----------------------------------------------\n\n');
+    end
+
+    lambda_surface(iL, :) = lambda_vec;   % λ for each f_target
+    Dacc_surface(iL, :)   = Dacc_vec;     % corresponding accumulated dispersion
+end
+
+% Convert for plotting
+lambda_surface_nm = lambda_surface * 1e9;  % [nm]
+L_km              = L_values / 1000;       % [km]
+f_GHz             = f_targets / 1e9;       % [GHz]
+
+%% Contour plot
+figure('Color','w');
+
+% Define wavelength contour levels [nm]
+lambda_levels = [1260:10:1290, 1290:5:1300, 1300:2.5:1310];
+lambda_levels = [100:-20:50, 50:-10:30,30:-5:0];
+
+% Contour plot
+contour(f_GHz, L_km, lambda_surface_nm, lambda_levels, ...
+    'LineWidth', 1.5, ...
+    'ShowText', 'on', ...
+    'LabelFormat', '%.0f nm');
+
+% Colormap and colorbar
+colormap((cbrewer2('RdYlGn',100)));
+colorbar;
+clim([0 100]);
+
+% Axis formatting
+xlabel('Signal Bandwidth [GHz]');
+ylabel('Fiber length [km]');
+legend('$\Delta \lambda$')
+
+% X-axis ticks at 56 : 16 : 150 GHz
+xticks(56:8:150);
+
+grid on; box on;
+
+
+%% Optional: overlay accumulated-dispersion contours
+if 0 
+    hold on;
+    [CS, h] = contour(f_GHz, L_km, Dacc_surface, 10, 'k--', 'LineWidth', 0.8);
+    clabel(CS, h, 'Color','k', 'FontSize',8);
+end
+
+function [lambda_vec, Dacc_vec] = lambda_for_first_null_full(f_target, L, lambda0, S0)
+% lambda_for_first_null_full  (stable, single-branch + validity checks)
+% --------------------------------------------------------------------
+% Computes the wavelength(s) at which the first IM/DD fading null
+% occurs at frequency/ies f_target using the full dispersion model:
+%
+%   D(lambda) = (S0/4)*(lambda - lambda0^4 / lambda^3)
+%
+% Restricted to the NORMAL-dispersion branch (λ < λ0),
+% and valid only in the O-band (1260–1360 nm).
+%
+% Inputs:
+%   f_target - scalar or vector of target null frequencies [Hz]
+%   L        - fiber length [m]
+%   lambda0  - zero-dispersion wavelength (ZDW) [m]
+%   S0       - dispersion slope at ZDW [ps/(nm²·km)]
+%
+% Outputs:
+%   lambda_vec - wavelength(s) [m] where first null occurs (clamped to O-band)
+%   Dacc_vec   - accumulated dispersion(s) [ps/nm] (NaN if out of valid range)
+% --------------------------------------------------------------------
+
+    c = physconst('lightspeed');
+    S0_si = S0 * 1e3;   % ps/(nm²·km) -> s/(m³)
+
+    % Define O-band boundaries (in meters)
+    lambda_min = 1255e-9;
+    lambda_max = 1361e-9;
+
+    % Force column vector
+    f_target = f_target(:);
+    N = numel(f_target);
+
+    lambda_vec = NaN(N,1);
+    Dacc_vec   = NaN(N,1);
+
+    for k = 1:N
+        RHS = c * 0.5 / (f_target(k)^2 * L);
+
+        % Normal-dispersion branch (λ < λ0)
+        fun = @(lambda) -(S0_si/4).*(lambda - (lambda0^4)./(lambda.^3)).*lambda.^2 - RHS;
+
+        % Limit the search to [λ_min, λ0)
+        try
+            lambda_sol = fzero(fun, [lambda_min, lambda0 * 0.999]);
+        catch
+            % If the zero is not within bounds, skip this point
+            lambda_sol = NaN;
+        end
+
+        % Validate solution
+        if isnan(lambda_sol) || lambda_sol < lambda_min || lambda_sol > lambda_max
+            lambda_vec(k) = NaN;
+            Dacc_vec(k)   = NaN;
+            continue
+        end
+
+        % Compute D(lambda) and accumulated dispersion
+        D_lambda = (S0_si/4) * (lambda_sol - (lambda0^4)/(lambda_sol^3)) / 1e-6; % ps/(nm·km)
+        Dacc_val = D_lambda * (L/1000);                                         % ps/nm
+
+        % Sanity bound on dispersion (avoid unphysical > ±100 ps/nm)
+        if abs(Dacc_val) > 100
+            lambda_vec(k) = NaN;
+            Dacc_vec(k)   = NaN;
+        else
+            lambda_vec(k) = lambda_sol;
+            Dacc_vec(k)   = Dacc_val;
+        end
+    end
+end

Functions/Theory/dispersion_first_notch_10km.m

@@ -0,0 +1,38 @@
+%% ------------------------------------------------------------
+% Plot: Maximum usable IM/DD bandwidth vs wavelength
+% ------------------------------------------------------------
+
+% Fiber and dispersion parameters
+lambda0 = 1310e-9;   % [m]
+S0      = 0.08;      % [ps/(nm²·km)]
+L       = 10000;      % [m]
+c       = physconst('lightspeed');
+
+% Wavelength range around ZDW
+lambda_vec = linspace(1250e-9, 1350e-9, 200);  % [m]
+
+% Compute D(lambda) using full model
+lambda_nm  = lambda_vec * 1e9;
+lambda0_nm = lambda0 * 1e9;
+D_lambda   = (S0/4) .* (lambda_nm - (lambda0_nm.^4) ./ (lambda_nm.^3));  % [ps/(nm·km)]
+
+% Convert D to [s/m²]
+D_si = D_lambda * 1e-6;  
+
+% Compute first null frequency (f₀) for each wavelength
+f_null = sqrt(c*(0.5) ./ (abs(D_si).*lambda_vec.^2*L));  % [Hz]
+
+% Plot
+figure('Color','w');
+plot(lambda_vec*1e9, f_null/1e9, 'LineWidth', 1.6);
+grid on; box on;
+xlabel('Wavelength [nm]');
+ylabel('First Fading Null Frequency [GHz]');
+title(sprintf('IM/DD Bandwidth Limit vs. Wavelength  (L = %.1f km)', L/1000));
+
+% Highlight useful bandwidth thresholds
+yline(25, '--', '25 GHz','Color',[0.4 0.4 0.4],'LabelHorizontalAlignment','left');
+yline(50, '--', '50 GHz','Color',[0.2 0.6 0.2],'LabelHorizontalAlignment','left');
+yline(100,'--', '100 GHz','Color',[0.6 0.2 0.2],'LabelHorizontalAlignment','left');
+
+legend('First fading notch (f_{null})','Location','best');

Functions/Theory/dispersion_power_fading.m

@@ -0,0 +1,165 @@
+%% Chromatic Dispersion Power Fading Demonstration
+% ------------------------------------------------------------
+% This script computes and visualizes power fading after
+% photodiode detection caused by chromatic dispersion in IM/DD links.
+%
+% It also determines the wavelength λ that produces the first
+% fading null at a specified RF frequency f_target using the
+% full physical dispersion model:
+%
+%   D(λ) = (S0/4) * (λ - λ0^4 / λ^3)
+%
+% and compares the analytic null frequency with simulation.
+% ------------------------------------------------------------
+
+% clear; close all; clc;
+
+%% Fiber and wavelength parameters
+lambda0  = 1310e-9;   % Zero-dispersion wavelength (ZDW) [m]
+S0       = 0.08;      % Dispersion slope at ZDW [ps/(nm^2·km)]
+L        = 10000;      % Fiber length [m]
+alpha_dB = 0;         % Attenuation [dB/m] (ignored here)
+
+%% Target null frequency
+f_targets = linspace(55e9,58e9,10);
+f_targets = 56e9;
+% f_targets = 80e9;
+% Compute wavelength that gives the first null at f_target
+[lambda_vec, Dacc_vec] = lambda_for_first_null_full(f_targets, L, lambda0, S0);
+% lambda_vec = 1293e-9;
+
+
+fprintf('\n----------------------------------------------\n');
+fprintf('   f_null [GHz]     lambda [nm]     Dacc [ps/nm]\n');
+fprintf('----------------------------------------------\n');
+fprintf('%10.1f    %8.2f       %+8.3f\n',[f_targets(:)/1e9, lambda_vec(:)*1e9, Dacc_vec(:)].');
+fprintf('----------------------------------------------\n\n');
+
+%% Frequency grid
+f_simu = 500e9;                % Simulation bandwidth [Hz]
+N_freq = 500000;
+faxis  = linspace(-f_simu/2, f_simu/2, N_freq);
+
+%% Derived fiber parameters
+c  = physconst('lightspeed');
+S0_si = S0 * 1e3;  % ps/(nm²·km) -> s/m³
+
+% Convert wavelengths to nm for the D(lambda) model
+lambda_nm  = lambda_vec(end) * 1e9;
+lambda0_nm = lambda0 * 1e9;
+
+% Dispersion parameter [ps/(nm·km)]
+D_lambda = (S0/4) * (lambda_nm - (lambda0_nm^4)/(lambda_nm^3));
+
+% Convert to [s/m²]
+D_si = D_lambda * 1e-6;
+
+% β2 in [s²/m]
+b2 = -D_si * lambda_vec(end)^2 / (2*pi*c);
+
+%% IM/DD intensity response (simulation)
+phi = 2*pi^2*b2*faxis.^2*L;
+H_field_pos = exp(-1j*phi);  % +f sideband
+H_field_neg = exp(+1j*phi);  % -f sideband
+H_intensity = 0.5 * (H_field_pos + H_field_neg);  % PD beating term
+H_sim = abs(H_intensity);
+
+%% Theoretical analytical IM/DD response
+phi = 2*pi^2 * abs(b2) * faxis.^2 * L;
+H_theoretical = abs(cos(phi));
+
+%% Analytic first null (for verification)
+f_null_analytic = sqrt(c*(0.5)/(abs(D_si)*lambda_vec(end)^2*L));
+fprintf('Analytic first null from D,λ,L: %.2f GHz\n\n', f_null_analytic/1e9);
+
+%% Plot
+cols = linspecer(5);
+figure('Color','w'); hold on; grid on; box on;
+plot(faxis*1e-9, 10*log10(H_sim), 'DisplayName','$|H_{sim}|$ (IM/DD simulation)','Color',cols(1,:));
+plot(faxis*1e-9, 10*log10(H_theoretical), 'DisplayName','|cos($\phi$)| (theory)','Color',cols(2,:),'LineStyle','--');
+xline(f_targets(end)/1e9,'k:','LineWidth',1.2,'DisplayName','Target null (56 GHz)');
+xline(f_null_analytic/1e9,'Color',[0.2 0.6 0.2],'LineStyle','-.','LineWidth',1.2,'DisplayName','Analytic null');
+xlabel('Frequency [GHz]');
+ylabel('Magnitude [dB]');
+title(sprintf('Power Fading for %.2f nm,  L = %.1f km',lambda_nm,L/1000));
+legend('Location','best'); ylim([-30 0]);
+
+%% Plot Bandwidth vs Lambda max
+
+figure();
+hold on;
+plot(lambda_vec.*1e6,f_targets.*1e-9)
+xlabel('wavelength');
+ylabel('max. Bandwidth')
+
+function [lambda_vec, Dacc_vec] = lambda_for_first_null_full(f_target, L, lambda0, S0)
+% lambda_for_first_null_full  (stable, single-branch + validity checks)
+% --------------------------------------------------------------------
+% Computes the wavelength(s) at which the first IM/DD fading null
+% occurs at frequency/ies f_target using the full dispersion model:
+%
+%   D(lambda) = (S0/4)*(lambda - lambda0^4 / lambda^3)
+%
+% Restricted to the NORMAL-dispersion branch (λ < λ0),
+% and valid only in the O-band (1260–1360 nm).
+%
+% Inputs:
+%   f_target - scalar or vector of target null frequencies [Hz]
+%   L        - fiber length [m]
+%   lambda0  - zero-dispersion wavelength (ZDW) [m]
+%   S0       - dispersion slope at ZDW [ps/(nm²·km)]
+%
+% Outputs:
+%   lambda_vec - wavelength(s) [m] where first null occurs (clamped to O-band)
+%   Dacc_vec   - accumulated dispersion(s) [ps/nm] (NaN if out of valid range)
+% --------------------------------------------------------------------
+
+    c = physconst('lightspeed');
+    S0_si = S0 * 1e3;   % ps/(nm²·km) -> s/(m³)
+
+    % Define O-band boundaries (in meters)
+    lambda_min = 1255e-9;
+    lambda_max = 1361e-9;
+
+    % Force column vector
+    f_target = f_target(:);
+    N = numel(f_target);
+
+    lambda_vec = NaN(N,1);
+    Dacc_vec   = NaN(N,1);
+
+    for k = 1:N
+        RHS = c * 0.5 / (f_target(k)^2 * L);
+
+        % Normal-dispersion branch (λ < λ0)
+        fun = @(lambda) -(S0_si/4).*(lambda - (lambda0^4)./(lambda.^3)).*lambda.^2 - RHS;
+
+        % Limit the search to [λ_min, λ0)
+        try
+            lambda_sol = fzero(fun, [lambda_min, lambda0 * 0.999]);
+        catch
+            % If the zero is not within bounds, skip this point
+            lambda_sol = NaN;
+        end
+
+        % Validate solution
+        if isnan(lambda_sol) || lambda_sol < lambda_min || lambda_sol > lambda_max
+            lambda_vec(k) = NaN;
+            Dacc_vec(k)   = NaN;
+            continue
+        end
+
+        % Compute D(lambda) and accumulated dispersion
+        D_lambda = (S0_si/4) * (lambda_sol - (lambda0^4)/(lambda_sol^3)) / 1e-6; % ps/(nm·km)
+        Dacc_val = D_lambda * (L/1000);                                         % ps/nm
+
+        % Sanity bound on dispersion (avoid unphysical > ±100 ps/nm)
+        if abs(Dacc_val) > 100
+            lambda_vec(k) = NaN;
+            Dacc_vec(k)   = NaN;
+        else
+            lambda_vec(k) = lambda_sol;
+            Dacc_vec(k)   = Dacc_val;
+        end
+    end
+end

Functions/Theory/dispersion_wavelength_notch.m

@@ -0,0 +1,32 @@
+%% Dependency f_null vs Delta_lambda
+lambda0 = 1310e-9;
+S0 = 0.09;       % ps/(nm²·km)
+L  = 10e3;       % m
+c  = physconst('lightspeed');
+
+% Convert slope to SI
+S0_si = S0 * 1e3;   % s/m³
+
+Delta_lambda = linspace(5e-9, 80e-9, 300); % [m] detuning
+f_null_2 = sqrt( c * 0.5 ./ (S0_si .* abs(Delta_lambda) .* lambda0.^2 .* L) );
+L  = 2e3;       % m
+f_null_10 = sqrt( c * 0.5 ./ (S0_si .* abs(Delta_lambda) .* lambda0.^2 .* L) );
+
+cols = cbrewer2('Paired',10);
+figure('Color','w');hold on
+cnt = 2;
+for L = 10%[2,5,10]
+    f_null_10 = sqrt( c * 0.5 ./ (S0_si .* abs(Delta_lambda) .* lambda0.^2 .* L*1e3) );
+    plot(1310-Delta_lambda*1e9, f_null_10/1e9, 'LineWidth',2,'DisplayName',sprintf('%d km',L),'Color',cols(cnt,:));
+    cnt = cnt+2;
+end
+yticks([56,75,90,112])
+tickse = 1310-[7.5, 12, 17, 31.5];
+xticks(flip(tickse));
+
+xlabel('$\Delta \lambda$ from ZDW [nm]');
+ylabel('$F_{null}$  [GHz]');
+grid on; box on;
+lim=1310-[5,35];
+xlim([lim(2) lim(1)]);
+ylim([40,130])

Functions/Theory/dispersion_wdm.m

@@ -0,0 +1,111 @@
+%% ============================================================
+%  IM/DD Fading Notch Design Map
+%  Shows λ_null vs. bandwidth (f_target) and fiber length (L)
+% ============================================================
+
+clear; close all; clc;
+
+%% Parameters
+lambda0 = 1310e-9;   % Zero-dispersion wavelength [m]
+S0      = 0.08;      % Dispersion slope at ZDW [ps/(nm²·km)]
+c       = physconst('lightspeed');
+
+% Frequency and length sweep
+f_targets = linspace(20e9, 140e9, 80);   % [Hz]  → x-axis
+L_values  = linspace(0.5e3, 12e3, 80);   % [m]   → y-axis
+
+% Preallocate result matrices
+lambda_surface = zeros(numel(L_values), numel(f_targets));
+Dacc_surface   = zeros(numel(L_values), numel(f_targets));
+
+%% Compute λ_null and Dacc for each (f_target, L)
+for iL = 1:numel(L_values)
+    L = L_values(iL);
+    [lambda_vec, Dacc_vec] = lambda_for_first_null_full(f_targets, L, lambda0, S0);
+    lambda_surface(iL, :) = lambda_vec;   % [m]
+    Dacc_surface(iL, :)   = Dacc_vec;     % [ps/nm]
+end
+
+%% Convert to display units
+lambda_surface_nm = lambda_surface * 1e9;  % [nm]
+L_km              = L_values / 1000;       % [km]
+f_GHz             = f_targets / 1e9;       % [GHz]
+
+%% ------------------------------------------------------------
+% Contour plot (λ_null as function of f_null and L)
+% ------------------------------------------------------------
+figure('Color','w');
+
+% Define wavelength contour levels [nm]
+lambda_levels = [1260:10:1290, 1290:5:1300, 1300:2:1310];
+
+contourf(f_GHz, L_km, lambda_surface_nm, lambda_levels, ...
+    'LineWidth', 1.5, ...
+    'ShowText', 'on', ...
+    'LabelFormat', '%1.1d nm');
+
+% Colormap and colorbar
+colormap(flip(cbrewer2('RdYlGn',100)));
+clim([1260 1310]);
+% c = colorbar;
+% ylabel(c, 'λ_{null} [nm]', 'Rotation', 90);
+
+% Axis formatting
+xlabel('Signal Bandwidth [GHz]');
+ylabel('Fiber length  L [km]');
+% X-axis ticks (every 16 GHz starting at 56 GHz)
+xticks(56:8:120);
+xlim([56,120])
+grid on; box on;
+
+%% Optional overlay: accumulated dispersion contours
+hold on;
+[CS, h] = contour(f_GHz, L_km, Dacc_surface, 10, 'k--', 'LineWidth', 0.8);
+clabel(CS, h, 'Color','k', 'FontSize',8);
+legend('λ_{null} contours','|D_{acc}| [ps/nm]','Location','best');
+
+%% ============================================================
+% Helper function: lambda_for_first_null_full
+% Stable, single-branch, clamped to O-band
+% ============================================================
+function [lambda_vec, Dacc_vec] = lambda_for_first_null_full(f_target, L, lambda0, S0)
+    c = physconst('lightspeed');
+    S0_si = S0 * 1e3;   % ps/(nm²·km) -> s/(m³)
+
+    % Define O-band boundaries (in meters)
+    lambda_min = 1260e-9;
+    lambda_max = 1360e-9;
+
+    % Force column vector
+    f_target = f_target(:);
+    N = numel(f_target);
+
+    lambda_vec = zeros(N,1);
+    Dacc_vec   = zeros(N,1);
+
+    for k = 1:N
+        RHS = c * 0.5 / (f_target(k)^2 * L);
+
+        % Normal-dispersion branch (λ < λ0)
+        fun = @(lambda) -(S0_si/4).*(lambda - (lambda0^4)./(lambda.^3)).*lambda.^2 - RHS;
+
+        % Solve within the normal-dispersion range
+        try
+            lambda_sol = fzero(fun, [lambda_min, lambda0 * 0.999]);
+        catch
+            lambda_sol = lambda_min;
+        end
+
+        % Clamp to O-band range
+        lambda_sol = min(max(lambda_sol, lambda_min), lambda_max);
+        lambda_vec(k) = lambda_sol;
+
+        % Compute D(lambda) and accumulated dispersion
+        D_lambda = (S0_si/4) * (lambda_sol - (lambda0^4)/(lambda_sol^3)) / 1e-6; % ps/(nm·km)
+        Dacc_val = D_lambda * (L/1000);  % ps/nm
+
+        % Clamp to physical range
+        Dacc_val = min(max(Dacc_val, -100), 100);
+        Dacc_vec(k) = Dacc_val;
+    end
+end

Functions/Theory/matched_filter_rrc.m

@@ -0,0 +1,120 @@
+%% Matched Filter SNR Demonstration (Correct Timing)
+% clear; close all; clc;
+
+%% Parameters
+M = 4;                    % QPSK
+numSymbols = 1e6;
+sps = 25;                 % samples per symbol
+rolloff = 0.5;
+EbNo_dB = 10;
+
+%% Generate random data
+data = randi([0 M-1], numSymbols, 1);
+txSym = qammod(data, M, 'UnitAveragePower', true);
+
+%% Root Raised Cosine filters
+span = 64;                 % filter span in symbols
+rrcTx = rcosdesign(rolloff, span, sps, 'sqrt');
+rrcRx = rrcTx;             % matched filter
+
+txSignal2 = ifft(fft(rrcTx).*fft(txSym));
+
+%% Transmit filtering (includes upsampling)
+txSignal = upfirdn(txSym, rrcTx, sps, 1);
+
+%% AWGN channel
+rxSignal = awgn(txSignal, EbNo_dB + 10*log10(sps), 'measured');
+
+%% Receiver matched filter
+rxFilt = conv(rxSignal, rrcRx, 'same');
+
+%% Symbol timing (group delay compensation)
+delay = span * sps / 2;    % total delay per filter is span*sps/2
+rxAligned = rxFilt(delay+1 : end-delay);
+
+%% Downsample to symbol rate
+rxSampled = rxAligned(1:sps:end);
+
+%% Align lengths
+L = min(length(rxSampled), length(txSym));
+rxSampled = rxSampled(1:L);
+txSym = txSym(1:L);
+
+%% Decision and BER
+rxSym = qamdemod(rxSampled, M, 'UnitAveragePower', true);
+[~, ber] = biterr(data(1:L), rxSym);
+
+%% Compute effective SNR
+snr_meas = 10*log10(mean(abs(txSym).^2) / mean(abs(txSym - rxSampled).^2));
+
+fprintf('Measured BER: %.3e  |  Effective SNR: %.2f dB\n', ber, snr_meas);
+
+
+%% Eye diagrams
+eyediagram(rxSignal(1:4000), 2*sps);
+title('Received Signal (Before Matched Filter)');
+eyediagram(rxFilt(1:4000), 2*sps);
+title('After Matched Filter (RRC)');
+
+%% --------------------------------------------------------------
+%% Spectrum analysis of shaped and filtered signals
+%% --------------------------------------------------------------
+
+Fs = sps;                        % normalized sample rate (symbol rate = 1)
+Nfft = 2^16;                     % FFT size for high resolution
+f = (-Nfft/2:Nfft/2-1)/Nfft * Fs; % normalized frequency axis (symbol-rate units)
+
+% Spectra
+S_tx = 20*log10(abs(fftshift(fft(txSignal, Nfft)))/max(abs(fft(txSignal, Nfft))));
+S_rx = 20*log10(abs(fftshift(fft(rxFilt, Nfft)))/max(abs(fft(rxFilt, Nfft))));
+
+% Unshaped (rectangular pulse) for comparison
+txRect_unf = upfirdn(txSym, ones(1, sps), sps, 1);
+S_rect = 20*log10(abs(fftshift(fft(txRect_unf, Nfft)))/max(abs(fft(txRect_unf, Nfft))));
+
+% Plot
+figure('Name','Spectrum after Pulse Shaping');
+plot(f, S_rect, '--', 'DisplayName','Rectangular pulse');
+hold on;
+plot(f, S_tx, 'LineWidth',1.4, 'DisplayName','RRC (TX)');
+plot(f, S_rx, 'LineWidth',1.4, 'DisplayName','After Matched Filter');
+grid on;
+xlabel('Normalized frequency (× symbol rate)');
+ylabel('Magnitude [dB]');
+title('Spectra Before and After RRC Pulse Shaping');
+legend('Location','best');
+xlim([-1.5 1.5]);
+ylim([-60 0]);
+
+
+%% --------------------------------------------------------------
+%% Visualization: RRC and Raised-Cosine Frequency Responses
+%% --------------------------------------------------------------
+
+% Frequency axis for plotting (normalized to symbol rate)
+Nfft = 4096;
+H_rrc = fftshift(fft(rrcTx, Nfft));
+H_rc  = H_rrc .* H_rrc;   % cascade of TX and RX RRC = full RC
+
+f = linspace(-0.5, 0.5, Nfft);  % normalized frequency (symbol-rate units)
+
+figure('Name','Raised Cosine Filter Characteristics');
+
+subplot(2,1,1);
+plot(f, 20*log10(abs(H_rrc)/max(abs(H_rrc))), 'LineWidth', 1.5);
+hold on;
+plot(f, 20*log10(abs(H_rc)/max(abs(H_rc))), '--', 'LineWidth', 1.5);
+grid on;
+xlabel('Normalized frequency (× symbol rate)');
+ylabel('Magnitude [dB]');
+title(sprintf('RRC (rolloff = %.2f) and Full RC Spectrum', rolloff));
+legend('Root Raised Cosine','Raised Cosine (TX×RX)','Location','best');
+ylim([-60 5]);
+
+subplot(2,1,2);
+t = (-span*sps/2 : span*sps/2) / sps; % time axis in symbol durations
+plot(t, rrcTx, 'LineWidth', 1.5);
+grid on;
+xlabel('Time [symbols]');
+ylabel('Amplitude');
+title('RRC Impulse Response');

Functions/Theory/power_fading.m

@@ -0,0 +1,40 @@
+%% ============================================================
+%  Minimal IM/DD Power Fading Plot
+% ============================================================
+
+
+%% Fiber and system parameters
+lambda0 = 1310e-9;       % zero-dispersion wavelength [m]
+lambda  = 1275e-9;       % operating wavelength [m]
+S0      = 0.08;          % dispersion slope [ps/(nm²·km)]
+L       = 10e3;          % fiber length [m]
+c       = physconst('lightspeed');
+
+%% Derived quantities
+S0_si = S0 * 1e3;                                    % → s/m³
+D_lambda = (S0/4) * (lambda*1e9 - (lambda0*1e9)^4/(lambda*1e9)^3); % ps/(nm·km)
+D_si = D_lambda * 1e-6;                              % → s/m²
+b2   = -D_si * lambda^2 / (2*pi*c);                  % s²/m
+
+%% Frequency grid
+f_max = 150e9;
+f = linspace(0, f_max, 4000);                        % [Hz]
+
+%% IM/DD transfer function (power fading)
+phi = 2*pi^2 * b2 * f.^2 * L;
+H = abs(cos(phi));
+
+%% Plot
+figure('Color','w');
+plot(f/1e9, 10*log10(H), 'LineWidth', 1.8);
+grid on; box on;
+xlabel('Frequency [GHz]');
+ylabel('Magnitude [dB]');
+title(sprintf('IM/DD Power Fading |H| for λ = %.1f nm, L = %.1f km', lambda*1e9, L/1000));
+ylim([-30 0]);
+
+%% Mark analytic first-null frequency
+f_null = sqrt(c*(0.5)/(abs(D_si)*lambda^2*L));
+xline(f_null/1e9, 'r--', 'LineWidth', 1.2, ...
+       'Label', sprintf('f_{null}=%.1f GHz', f_null/1e9), ...
+       'LabelOrientation', 'horizontal', 'LabelVerticalAlignment', 'bottom');

Functions/beautifyBERplot.m

@@ -1,37 +1,113 @@
-function beautifyBERplot()
-    % BEAUTIFYBERPLOT Enhances a BER plot for publication-quality figures.
+function beautifyBERplot(options)
+% BEAUTIFYBERPLOT  Enhances BER-style plots for publication-quality figures.
+% Supports automatic smoothing and trend-line overlay.
+%
+% Usage examples:
+%   beautifyBERplot;                                 % default
+%   beautifyBERplot("polyfit",1);                    % add polynomial fit
+%   beautifyBERplot("polyfit",1,"fitmethod","pchip") % piecewise cubic fit
+%
+% Supported fitmethod options: 'polyfit', 'smoothingspline', 'loess', 'pchip'
+
+arguments
+    options.logscale (1,1) logical = 1
+    options.setmarkers (1,1) logical = 1;
+    options.polyfit (1,1) logical = 0
+    options.polyorder (1,1) double = 2
+    options.fitmethod (1,1) string = "polyfit"  % choose fit type
+end
+
+% --- find all line objects in current axes
+lines = findall(gca, 'Type', 'Line');
+markers = {'o', 's', 'd', '^', 'v', '>', '<', 'p', 'h'}; 
+num_markers = length(markers);
+
+% --- style all lines consistently
+for i = 1:length(lines)
+    lines(i).LineWidth = 1.2;
+    % lines(i).LineStyle = '-';
+    if options.setmarkers == 1
+        if string(lines(i).Marker) == "none"
+            lines(i).Marker = markers{mod(i-1, num_markers) + 1};
+        end
+        lines(i).MarkerSize = 4;
+        lines(i).MarkerFaceColor = lines(i).Color;
+    end
+end
+
+% --- optional smoothing/fitting overlay
+if options.polyfit
+    hold on
+    for i = 1:length(lines)
+        x = lines(i).XData;
+        y = lines(i).YData;
+        valid = isfinite(x) & isfinite(y);
+        if sum(valid) < options.polyorder + 1
+            continue;
+        end
+
+        xf = linspace(min(x(valid)), max(x(valid)), 200);
+
+        % ----- choose fitting method -----
+        switch lower(options.fitmethod)
+            case "polyfit"
+                p = polyfit(x(valid), y(valid), options.polyorder);
+                yf = polyval(p, xf);
+
+            case "smoothingspline"
+                try
+                    f = fit(x(valid)', y(valid)', 'smoothingspline');
+                    yf = feval(f, xf);
+                catch
+                    yf = interp1(x(valid), y(valid), xf, 'pchip');
+                end
+
+            case "loess"
+                yf = smooth(x(valid), y(valid), 0.2, 'loess');
+                yf = interp1(x(valid), yf, xf, 'linear', 'extrap');
+
+            case "pchip"
+                yf = interp1(x(valid), y(valid), xf, 'pchip');
+
+            otherwise
+                warning('Unknown fitmethod "%s". Using polyfit.', options.fitmethod);
+                p = polyfit(x(valid), y(valid), options.polyorder);
+                yf = polyval(p, xf);
+        end
+
+        % --- lightened color for fit overlay
+        lightcol = lines(i).Color + 0.4 * (1 - lines(i).Color);
+        lightcol(lightcol > 1) = 1;
+
+        plot(xf, yf, '-', 'Color', lightcol, ...
+            'LineWidth', 0.7, 'Marker', 'none', ...
+            'HandleVisibility','off');
+    end
+    hold off
+end
+
+% --- axis scaling and cosmetics
+if options.logscale
+    set(gca, 'YScale', 'log');
+end
+
+% --- Figure size in centimeters ---
+% width_pt  = 500;
+% height_pt = 300;
+% 
+% pt2cm = 0.03514598;   % TeX point → cm
+% width_cm  = width_pt  * pt2cm;   % = 8.85 cm
+% height_cm = height_pt * pt2cm;   % = 2.81 cm
+% 
+% set(gcf, 'Units', 'centimeters', 'Position', [2 2 width_cm height_cm]);
+% set(gcf, 'PaperUnits', 'centimeters', 'PaperPosition', [0 0 width_cm height_cm]);
+
+% --- Formatting ---
+set(findall(gca, '-property', 'Interpreter'), 'Interpreter', 'latex');
+set(gcf, 'Color', 'w');
+set(gca, 'Box', 'on', 'LineWidth', 0.8);
+grid on;
+set(gca, 'FontSize', 10, 'FontName', 'Latin Modern Roman');
+
 
-    % Set line properties for all current plot lines
-    lines = findall(gca, 'Type', 'Line');
-    markers = {'o', 's', 'd', '^', 'v', '>', '<', 'p', 'h'}; % Define marker styles
-    num_markers = length(markers);
-    
-    for i = 1:length(lines)
-        lines(i).LineWidth = 1.3;  % Thicker line width
-        %lines(i).LineStyle = '-';  % Solid lines for simplicity
-        % if string(lines(i).Marker) == "none"
-        %     lines(i).Marker = markers{mod(i-1, num_markers) + 1}; % Assign markers cyclically
-        % end
-        lines(i).MarkerSize = 4;   % Marker size
-        lines(i).MarkerFaceColor = 'auto'; % Use line color for marker face
-    end
-
-    % Change all text interpreters to LaTeX
-    set(findall(gca, '-property', 'Interpreter'), 'Interpreter', 'latex');
-
-    % Set figure background to white
-    set(gcf, 'Color', 'w');
-
-    
-    % Set logarithmic scale for y-axis, but only if it makes sense.
-    % If this is not always desired, you could condition this on the presence of lines or data.
-    set(gca, 'YScale', 'log');
-
-    % Customize grid and box appearance
-    set(gca, 'Box', 'on', 'LineWidth', 0.8);  % Thicker border
-    grid on;
-    % grid minor;
-
-    % Adjust font size and style for better readability
-    set(gca, 'FontSize', 10, 'FontName', 'Times New Roman');
 end

Functions/convert_freq_lambda.m

@@ -0,0 +1,62 @@
+%% ============================================================
+%  Wavelength–Frequency Conversion Utilities
+% ============================================================
+
+% Example usage:
+%   f = lambda2freq(1310e-9);              % 1310 nm -> Hz
+%   lambda = freq2lambda(224e12);          % 224 THz -> m
+%   delta_lambda_nm = df2dlambda(224e12, 400e9); % 400 GHz @ 224 THz -> nm
+%   delta_freq_GHz  = dlambda2df(1310e-9, 3.45); % 3.45 nm @ 1310 nm -> GHz
+
+%% ---- Core conversion functions ----
+function f = lambda2freq(lambda)
+% lambda2freq Convert wavelength [m] → frequency [Hz]
+    c = physconst('lightspeed');
+    f = c ./ lambda;
+end
+
+function lambda = freq2lambda(f)
+% freq2lambda Convert frequency [Hz] → wavelength [m]
+    c = physconst('lightspeed');
+    lambda = c ./ f;
+end
+
+%% ---- Differential conversions ----
+function d_lambda = df2dlambda(f_center, d_f)
+% df2dlambda Convert frequency spacing Δf [Hz] → wavelength spacing Δλ [m]
+% around a given center frequency f_center [Hz].
+% Uses first-order differential: Δλ ≈ (c / f^2) * Δf
+
+    c = physconst('lightspeed');
+    d_lambda = (c ./ (f_center.^2)) .* d_f;
+end
+
+function d_f = dlambda2df(lambda_center, d_lambda)
+% dlambda2df Convert wavelength spacing Δλ [m] → frequency spacing Δf [Hz]
+% around a given center wavelength λ_center [m].
+% Uses first-order differential: Δf ≈ (c / λ^2) * Δλ
+
+    c = physconst('lightspeed');
+    d_f = (c ./ (lambda_center.^2)) .* d_lambda;
+end
+
+%% ============================================================
+%  Example section (can be commented out)
+% ============================================================
+
+if ~isdeployed
+    fprintf('--- Example conversions ---\n');
+
+    lambda_nm = 1310;                 % nm
+    lambda = lambda_nm * 1e-9;        % m
+    f = lambda2freq(lambda);          % Hz
+    fprintf('λ = %.1f nm  →  f = %.3f THz\n', lambda_nm, f/1e12);
+
+    d_f = 2000e9;                      % 400 GHz spacing
+    d_lambda = df2dlambda(f, d_f);    % [m]
+    fprintf('Δf = %.0f GHz @ %.1f nm  →  Δλ = %.3f nm\n', d_f/1e9, lambda_nm, d_lambda*1e9);
+
+    % Verify reverse direction
+    d_f_back = dlambda2df(lambda, d_lambda);
+    fprintf('Δλ = %.3f nm @ %.1f nm  →  Δf = %.0f GHz\n', d_lambda*1e9, lambda_nm, d_f_back/1e9);
+end

Libs/wesanderson_colors/WesPalette.m

@@ -0,0 +1,133 @@
+classdef WesPalette
+    % WESPALETTE  Wes Anderson color palettes with auto-completion
+    % Usage:
+    %   cmap = WesPalette.Zissou1.rgb()
+    %   cmap = WesPalette.Zissou1.rgb(3)
+
+    % https://github.com/karthik/wesanderson?tab=readme-ov-file
+
+    enumeration
+        BottleRocket1
+        BottleRocket2
+        Rushmore1
+        Rushmore
+        Royal1
+        Royal2
+        Zissou1
+        Zissou1Continuous
+        Darjeeling1
+        Darjeeling2
+        Chevalier1
+        FantasticFox1
+        Moonrise1
+        Moonrise2
+        Moonrise3
+        Cavalcanti1
+        GrandBudapest1
+        GrandBudapest2
+        IsleofDogs1
+        IsleofDogs2
+        FrenchDispatch
+        AsteroidCity1
+        AsteroidCity2
+        AsteroidCity3
+    end
+
+    methods
+        function cmap = rgb(obj, n)
+            % Return palette as Nx3 RGB colormap [0–1]
+
+            hex = obj.hex();
+
+            rgb = hex2rgb(hex);
+
+            if nargin == 2
+                if n > size(rgb,1)
+                    error('Requested %d colors, but only %d available.', ...
+                          n, size(rgb,1))
+                end
+                cmap = rgb(1:n,:);
+            else
+                cmap = rgb;
+            end
+        end
+    end
+
+    methods (Access = private)
+        function hex = hex(obj)
+            % Internal HEX storage
+
+            switch obj
+                case WesPalette.BottleRocket1
+                    hex = {'#A42820','#5F5647','#9B110E','#3F5151','#4E2A1E','#550307','#0C1707'};
+
+                case WesPalette.BottleRocket2
+                    hex = {'#FAD510','#CB2314','#273046','#354823','#1E1E1E'};
+
+                case {WesPalette.Rushmore1, WesPalette.Rushmore}
+                    hex = {'#E1BD6D','#EABE94','#0B775E','#35274A','#F2300F'};
+
+                case WesPalette.Royal1
+                    hex = {'#899DA4','#C93312','#FAEFD1','#DC863B'};
+
+                case WesPalette.Royal2
+                    hex = {'#9A8822','#F5CDB4','#F8AFA8','#FDDDA0','#74A089'};
+
+                case WesPalette.Zissou1
+                    hex = {'#3B9AB2','#78B7C5','#EBCC2A','#E1AF00','#F21A00'};
+
+                case WesPalette.Zissou1Continuous
+                    hex = {'#3A9AB2','#6FB2C1','#91BAB6','#A5C2A3','#BDC881', ...
+                           '#DCCB4E','#E3B710','#E79805','#EC7A05','#EF5703','#F11B00'};
+
+                case WesPalette.Darjeeling1
+                    hex = {'#FF0000','#00A08A','#F2AD00','#F98400','#5BBCD6'};
+
+                case WesPalette.Darjeeling2
+                    hex = {'#ECCBAE','#046C9A','#D69C4E','#ABDDDE','#000000'};
+
+                case WesPalette.Chevalier1
+                    hex = {'#446455','#FDD262','#D3DDDC','#C7B19C'};
+
+                case WesPalette.FantasticFox1
+                    hex = {'#DD8D29','#E2D200','#46ACC8','#E58601','#B40F20'};
+
+                case WesPalette.Moonrise1
+                    hex = {'#F3DF6C','#CEAB07','#D5D5D3','#24281A'};
+
+                case WesPalette.Moonrise2
+                    hex = {'#798E87','#C27D38','#CCC591','#29211F'};
+
+                case WesPalette.Moonrise3
+                    hex = {'#85D4E3','#F4B5BD','#9C964A','#CDC08C','#FAD77B'};
+
+                case WesPalette.Cavalcanti1
+                    hex = {'#D8B70A','#02401B','#A2A475','#81A88D','#972D15'};
+
+                case WesPalette.GrandBudapest1
+                    hex = {'#F1BB7B','#FD6467','#5B1A18','#D67236'};
+
+                case WesPalette.GrandBudapest2
+                    hex = {'#E6A0C4','#C6CDF7','#D8A499','#7294D4'};
+
+                case WesPalette.IsleofDogs1
+                    hex = {'#9986A5','#79402E','#CCBA72','#0F0D0E','#D9D0D3','#8D8680'};
+
+                case WesPalette.IsleofDogs2
+                    hex = {'#EAD3BF','#AA9486','#B6854D','#39312F','#1C1718'};
+
+                case WesPalette.FrenchDispatch
+                    hex = {'#90D4CC','#BD3027','#B0AFA2','#7FC0C6','#9D9C85'};
+
+                case WesPalette.AsteroidCity1
+                    hex = {'#0A9F9D','#CEB175','#E54E21','#6C8645','#C18748'};
+
+                case WesPalette.AsteroidCity2
+                    hex = {'#C52E19','#AC9765','#54D8B1','#B67C3B','#175149','#AF4E24'};
+
+                case WesPalette.AsteroidCity3
+                    hex = {'#FBA72A','#D3D4D8','#CB7A5C','#5785C1'};
+            end
+        end
+    end
+end

Libs/wesanderson_colors/hex2rgb.m

@@ -0,0 +1,20 @@
+function rgb = hex2rgb(hex)
+% HEX2RGB Convert HEX color codes to RGB [0–1]
+
+if ischar(hex)
+    hex = {hex};
+end
+
+n = numel(hex);
+rgb = zeros(n,3);
+
+for k = 1:n
+    h = hex{k};
+    h = strrep(h,'#','');
+    rgb(k,1) = hex2dec(h(1:2));
+    rgb(k,2) = hex2dec(h(3:4));
+    rgb(k,3) = hex2dec(h(5:6));
+end
+
+rgb = rgb / 255;
+end

Libs/wesanderson_colors/minimal_example_wespalette.m

@@ -0,0 +1,64 @@
+x = -10:2:25;   % Input power [dBm]
+
+y1 = 1e-5 * 10.^(0.12*x);             % Dispersion-only
+y2 = 1e0 ./ (1 + exp(-0.4*(x-12)));  % NLPN
+y3 = 1e-6 * 10.^(0.45*x);             % RP on gamma
+y4 = 1e-2 * 10.^(0.18*(x-8));         % RP on beta2
+
+cmap = WesPalette.AsteroidCity1.rgb(4);
+cmap = linspecer(4);
+figure1=figure(202998);clf;hold on
+lw = 0.8; ms = 4;
+plot(x,y1,'LineWidth',lw,'Color',cmap(1,:),'Marker','o','MarkerEdgeColor',cmap(1,:),'MarkerFaceColor',[1,1,1],'MarkerSize',ms); 
+plot(x,y2,'LineWidth',lw,'Color',cmap(2,:),'Marker','square','MarkerEdgeColor',cmap(2,:),'MarkerFaceColor',[1,1,1],'MarkerSize',ms);
+plot(x,y3,'LineWidth',lw,'Color',cmap(3,:),'Marker','o','MarkerEdgeColor',cmap(3,:),'MarkerFaceColor',[1,1,1],'MarkerSize',ms);
+plot(x,y4,'LineWidth',lw,'Color',cmap(4,:),'Marker','o','MarkerEdgeColor',cmap(4,:),'MarkerFaceColor',[1,1,1],'MarkerSize',ms);
+yline(3.8e-3)
+
+grid on
+xlabel('Input power [dBm]')
+ylabel('NSD ($\%$)')
+legend({'Dispersion','NLPN','RP','RP on $\beta_2$'}, ...
+       'Location','best')
+
+grid off
+set(gca,'MinorGridLineWidth',0.5);
+set(gca,'GridLineWidth',0.5,'GridLineStyle','--','GridColor',[0.9,0.9,0.9]);
+
+set(gca,'FontSize',12,'YScale','log');
+ylim([1e-6 1e3])
+xlim([-10 23])
+
+% % Create textarrow
+% annotation(figure1,'textarrow',[0.564444444444444 0.548148148148148],...
+%     [0.768523809523809 0.63047619047619],'String',{'(A)'});
+% 
+% % Create doublearrow
+% annotation(figure1,'doublearrow',[0.724444444444444 0.699259259259259],...
+%     [0.854238095238095 0.723809523809524]);
+% 
+% % Create line
+% annotation(figure1,'line',[0.700740740740741 0.699259259259259],...
+%     [0.554285714285714 0.405714285714286]);
+% 
+% % Create textbox
+% annotation(figure1,'textbox',...
+%     [0.578777777777778 0.194285714285714 0.104185185185185 0.0685714285714286],...
+%     'String',{'BOX'},...
+%     'FitBoxToText','off');
+
+% 
+fig_path = 'C:\Users\Silas\Documents\Dissertation\00_Examples\tikz\textfig.tikz';
+matlab2tikz(fig_path, ...
+    'width','\fwidth', ...
+    'height','\fheight', ...
+    'showInfo',false, ...
+    'extraAxisOptions',{ ...
+        'legend style={font=\footnotesize}', ...
+        'xlabel style={font=\color{white!15!black},font=\small},',...
+        'ylabel style={font=\color{white!15!black},font=\small},',...
+        'legend columns=1', ...
+        'every axis/.append style={font=\scriptsize}',...
+    'legend columns=2',...
+    'legend style={at={(0.02,0.98)},font=\footnotesize,draw=black!60,rounded corners=2pt,inner sep=1pt,fill=white,column sep=6pt,anchor= north west}',...
+    });

Libs/wesanderson_colors/wes_palettes.m

@@ -0,0 +1,79 @@
+function palettes = wes_palettes()
+% WES_PALETTES  Full Wes Anderson color palette collection for MATLAB
+% Colors are stored as HEX and converted to RGB on demand.
+
+palettes = struct();
+
+palettes.BottleRocket1 = { ...
+    '#A42820', '#5F5647', '#9B110E', '#3F5151', '#4E2A1E', '#550307', '#0C1707'};
+
+palettes.BottleRocket2 = { ...
+    '#FAD510', '#CB2314', '#273046', '#354823', '#1E1E1E'};
+
+palettes.Rushmore1 = { ...
+    '#E1BD6D', '#EABE94', '#0B775E', '#35274A', '#F2300F'};
+
+palettes.Rushmore = palettes.Rushmore1;
+
+palettes.Royal1 = { ...
+    '#899DA4', '#C93312', '#FAEFD1', '#DC863B'};
+
+palettes.Royal2 = { ...
+    '#9A8822', '#F5CDB4', '#F8AFA8', '#FDDDA0', '#74A089'};
+
+palettes.Zissou1 = { ...
+    '#3B9AB2', '#78B7C5', '#EBCC2A', '#E1AF00', '#F21A00'};
+
+palettes.Zissou1Continuous = { ...
+    '#3A9AB2', '#6FB2C1', '#91BAB6', '#A5C2A3', '#BDC881', ...
+    '#DCCB4E', '#E3B710', '#E79805', '#EC7A05', '#EF5703', '#F11B00'};
+
+palettes.Darjeeling1 = { ...
+    '#FF0000', '#00A08A', '#F2AD00', '#F98400', '#5BBCD6'};
+
+palettes.Darjeeling2 = { ...
+    '#ECCBAE', '#046C9A', '#D69C4E', '#ABDDDE', '#000000'};
+
+palettes.Chevalier1 = { ...
+    '#446455', '#FDD262', '#D3DDDC', '#C7B19C'};
+
+palettes.FantasticFox1 = { ...
+    '#DD8D29', '#E2D200', '#46ACC8', '#E58601', '#B40F20'};
+
+palettes.Moonrise1 = { ...
+    '#F3DF6C', '#CEAB07', '#D5D5D3', '#24281A'};
+
+palettes.Moonrise2 = { ...
+    '#798E87', '#C27D38', '#CCC591', '#29211F'};
+
+palettes.Moonrise3 = { ...
+    '#85D4E3', '#F4B5BD', '#9C964A', '#CDC08C', '#FAD77B'};
+
+palettes.Cavalcanti1 = { ...
+    '#D8B70A', '#02401B', '#A2A475', '#81A88D', '#972D15'};
+
+palettes.GrandBudapest1 = { ...
+    '#F1BB7B', '#FD6467', '#5B1A18', '#D67236'};
+
+palettes.GrandBudapest2 = { ...
+    '#E6A0C4', '#C6CDF7', '#D8A499', '#7294D4'};
+
+palettes.IsleofDogs1 = { ...
+    '#9986A5', '#79402E', '#CCBA72', '#0F0D0E', '#D9D0D3', '#8D8680'};
+
+palettes.IsleofDogs2 = { ...
+    '#EAD3BF', '#AA9486', '#B6854D', '#39312F', '#1C1718'};
+
+palettes.FrenchDispatch = { ...
+    '#90D4CC', '#BD3027', '#B0AFA2', '#7FC0C6', '#9D9C85'};
+
+palettes.AsteroidCity1 = { ...
+    '#0A9F9D', '#CEB175', '#E54E21', '#6C8645', '#C18748'};
+
+palettes.AsteroidCity2 = { ...
+    '#C52E19', '#AC9765', '#54D8B1', '#B67C3B', '#175149', '#AF4E24'};
+
+palettes.AsteroidCity3 = { ...
+    '#FBA72A', '#D3D4D8', '#CB7A5C', '#5785C1'};
+
+end

projects/ECOC_2025/dsp_run_id.m

@@ -243,7 +243,6 @@ for occ = 1:record_realizations
 
         fprintf("BER DB: %.2e \n",dbenc_package{occ}.resultsDBsignaling.BER);
 
-
     end
 
 

projects/ECOC_2025/load_signal_standalone.m

@@ -1,34 +1,20 @@
 
+db = DBHandler("type","mysql","dataBase",'labor');
+
 
+fp = QueryFilter();
+% fp.where('Runs', 'run_id','EQUALS', 987);
+M = 4;
+fp.where('Runs', 'pam_level','EQUALS', M);     
+fp.where('Runs', 'symbolrate','EQUALS', 112e9);
+fp.where('Runs', 'fiber_length','EQUALS', 0);   
+fp.where('Runs', 'is_mpi','EQUALS', 1);                
+fp.where('Runs', 'interference_path_length','EQUALS', 70);      
+% fp.where('Runs', 'loop_id','GREATER_THAN', 11);   
+fp.where('Runs', 'sir','EQUALS',20);  
 
-savePath = 'Z:\2025\ECOC Silas\ecoc_2025\';
-databasePath = 'C:\Users\Silas\Documents\MATLAB\imdd_simulation\projects\ECOC_2025\';
-database_name = 'ecoc2025_loops.db';
-db = DBHandler("type","mysql");
-% db = DBHandler("pathToDB", [databasePath, database_name],"type","sqlite");
 
-filterParams = db.tables;
-% filterParams.Configurations = struct('run_id', run_id);
-filterParams.Configurations = struct( ...
-    'symbolrate',      112e9, ... %[224,336,360,390,420,448]
-    'fiber_length',     0, ...
-    'db_mode',        '"no_db"', ...
-    'interference_attenuation', 4, ...
-    'interference_path_length', 300, ...
-    'is_mpi',           1, ...
-    'pam_level',        4, ...
-    'wavelength',       1310, ...
-    'precomp_amp',      [], ...
-    'signal_attenuation', [], ...
-    'v_awg',            [], ...
-    'v_bias',           [] ...
-    );
-
-selectedFields = {'Runs.run_id','Runs.tx_bits_path','Runs.tx_signal_path','Runs.tx_symbols_path','Runs.rx_sync_path','Runs.rx_raw_path',...
-    'Configurations.db_mode','Configurations.pam_level','Configurations.bitrate','Configurations.symbolrate','Configurations.fiber_length','Configurations.wavelength','Configurations.precomp_amp','Measurements.power_rop','Configurations.v_bias',...
-    'Configurations.interference_attenuation', 'Measurements.power_mpi_interference'};
-
-[dataTable,sql_query] = db.queryDB(filterParams, selectedFields);
+[dataTable,sql_query] = db.queryDB(fp, db.getTableFieldNames('Runs'));
 
 [~, uniqueIdx] = unique(dataTable.run_id);  % Get unique run_id indices
 
@@ -55,7 +41,7 @@ for i = 1:size(dataTable,1)
 
     Scpe_sig_raw = load([savePath, char(dataTable_.rx_raw_path(1))]);
     Scpe_sig_raw = Scpe_sig_raw.Scpe_sig_raw;
-    % Scpe_sig_raw.plot("displayname",['Scope Signal (Run ID: ',num2str(dataTable_.run_id)],"fignum",dataTable_.run_id,"clear",0);
+    Scpe_sig_raw.plot("displayname",['Scope Signal (Run ID: ',num2str(dataTable_.run_id)],"fignum",dataTable_.run_id,"clear",0);
 
 
     Scpe_sig_resampled = Scpe_sig_raw.resample("fs_in",Scpe_sig_raw.fs,"fs_out",fsym);

projects/ECOC_2025/measure_run_script.m

@@ -8,14 +8,14 @@ end
 precomp_path = "C:\Users\sioe\Documents\MATLAB\imdd_simulation\projects\ECOC_2025\";
 precomp_fn = "precomp_1km_1mm_cable_70ghz_pd_shf_t850_2p8_bias_shot2";
 precomp_mode = 2; %0=do nothing ; 1= measure; 2=precomp active
-run_lab_automation = 1;
+run_lab_automation = 0;
 
 %% Configuration
 
 conf = confRequest;
 referenceFields = fieldnames(db.tables.Configurations);
 
-assert(isequal(fieldnames(conf), referenceFields), 'Fieldnames do not match the reference structure!');
+% assert(isequal(fieldnames(conf), referenceFields), 'Fieldnames do not match the reference structure!');
 
 %% Lab Automation
 
@@ -28,7 +28,6 @@ if run_lab_automation
         [v_bias_meas,i_bias_meas]=dcs.readVals();
     end
     % Laser
-
     if (any(confPrev.laser_power ~= conf.laser_power)) || (~exist('laser','var')) || (isempty(confPrev.laser_power))
         laser = Exfo_laser("mainframe_channel",1,"safety_mode",0,"connection_id",'10','lab_interface','gpib');
         laser.setWavelength(conf.wavelength);
@@ -42,7 +41,7 @@ if run_lab_automation
     end
     voa.set('value',[0,conf.pd_in_desired,conf.signal_attenuation,conf.interference_attenuation]);
 
-    if voa.power_state(3) > -24 || voa.power_state(1)+ voa.atten_state(4) > -38
+    if voa.power_state(3) > -24 || voa.power_state(1)+ voa.atten_state(4) > -37
         holdAndShowValue
     end
 
@@ -51,15 +50,56 @@ if run_lab_automation
         pdfa = Thor_PDFA("safety_mode",0,"serialport_number",'COM15');
     end
 
-    % Construct AWG and Scope Modules %%%%%%
-    fdac        = 224e9;
-    fadc        = 160e9;
-    Scp = ScopeKeysight("model","DSAZ634A",'autoscale',0,"fadc","GSa_160","channel",[0,0,1,0],"recordLen",recordlen,"removeDC",1,"extRef",1);
-    Awg = AwgKeysight("model","M8199A_ILV","fdac",fdac,"scaletodac",[1,1],"skews",[0,0],"voltages",[0,conf.v_awg]);
-    A2S = Awg2Scope(Awg,Scp,[0,0,3,0],"waitUntilClick",0);
-
 end
 
+% if conf.symbolrate == 72e9
+%     if conf.pam_level == 2
+%         conf.v_awg = 0.55;
+%         conf.pulsef_alpha = 0.6;
+%     elseif conf.pam_level == 4
+%         conf.v_awg = 0.55;
+%         conf.pulsef_alpha =0.6;
+%     elseif conf.pam_level == 6
+%         conf.v_awg = 0.55;
+%         conf.pulsef_alpha = 0.6;
+%     elseif conf.pam_level == 8
+%         conf.v_awg = 0.55;
+%         conf.pulsef_alpha = 0.6;
+%     end
+% elseif conf.symbolrate == 96e9
+%     if conf.pam_level == 2
+%         conf.v_awg = 0.8;
+%         conf.pulsef_alpha = 0.2;
+%     elseif conf.pam_level == 4
+%         conf.v_awg = 0.8;
+%         conf.pulsef_alpha = 0.2;
+%     elseif conf.pam_level == 6
+%         conf.v_awg = 0.8;
+%         conf.pulsef_alpha = 0.2;
+%     elseif conf.pam_level == 8
+%         conf.v_awg = 0.8;
+%         conf.pulsef_alpha = 0.2;
+%     end
+% elseif conf.symbolrate == 112e9
+%     if conf.pam_level == 2
+%         conf.v_awg = 0.85;
+%         conf.pulsef_alpha = 0.2;
+%     elseif conf.pam_level == 4
+%         conf.v_awg = 0.85;
+%         conf.pulsef_alpha = 0.2;
+%     elseif conf.pam_level == 6
+%         conf.v_awg = 0.85;
+%         conf.pulsef_alpha = 0.2;
+%     elseif conf.pam_level == 8
+%         conf.v_awg = 0.85;
+%         conf.pulsef_alpha = 0.2;
+%     end
+% end
+
+awg_upload_required = 1;
+if any(confPrev.pam_level == conf.pam_level) && any(confPrev.symbolrate == conf.symbolrate)
+    awg_upload_required = 0;
+end
 
 %% Signal generation and transmission
 if awg_upload_required || isempty(loop_id)
@@ -70,6 +110,13 @@ if awg_upload_required || isempty(loop_id)
         sequence_order = min(18,sequence_order);
     end
 
+    % Construct AWG and Scope Modules %%%%%%
+    fdac        = 224e9;
+    fadc        = 160e9;
+    Scp = ScopeKeysight("model","DSAZ634A",'autoscale',0,"fadc","GSa_160","channel",[0,0,1,0],"recordLen",recordlen,"removeDC",1,"extRef",1);
+    Awg = AwgKeysight("model","M8199A_ILV","fdac",fdac,"scaletodac",[1,1],"skews",[0,0],"voltages",[0,conf.v_awg]);
+    A2S = Awg2Scope(Awg,Scp,[0,0,3,0],"waitUntilClick",0);
+
     Pamsource = PAMsource(...
         "fsym",conf.symbolrate,"M",conf.pam_level,"order",sequence_order,"useprbs",1,...
         "fs_out",fdac,...
@@ -80,11 +127,11 @@ if awg_upload_required || isempty(loop_id)
         "randkey",1,...
         "duobinary_mode", conf.db_mode);
 
-    conf.pam_source     = Pamsource;
-    confPrev            = conf;
-
     [Digi_sig,Symbols,Bits] = Pamsource.process();
 
+    figure(10);clf;
+    Digi_sig.spectrum("displayname",'Tx Pulse Shaped','fignum',10,'normalizeTo0dB',1);
+
 
     %%%%% Precompensation Routine %%%%%%
     if precomp_mode == 1 % measure channel
@@ -95,11 +142,13 @@ if awg_upload_required || isempty(loop_id)
         Digi_sig = precomp_est.precomp(Digi_sig,'maxampdb',conf.precomp_amp,'loadPath',precomp_path,'fileName',precomp_fn);
     end
 
-    % Digi_sig.spectrum("displayname",'Tx Spectrum','fignum',10,'normalizeTo0dB',0);
+    Digi_sig.spectrum("displayname",'Tx Pre-Emphasized','fignum',10,'normalizeTo0dB',1);
 
     %%%%% Resample to DAC rate %%%%%%
     Digi_sig = Digi_sig.resample("fs_out",Awg.fdac);
 
+    Digi_sig.spectrum("displayname",'Tx Resampled','fignum',10,'normalizeTo0dB',1);
+
     % [~,~,Scpe_sig_raw,~] = A2S.process("signal2",Digi_sig);
 
     Awg.upload("signal2",Digi_sig);
@@ -114,6 +163,8 @@ if precomp_mode == 1
     return; % End routine if precomp mode is active
 end
 
+confPrev            = conf;
+
 %% Save static TX data (only once)
 currentTime = datetime('now', 'Format', 'yyyyMMdd_HHmmss');
 timeStr = char(currentTime);
@@ -143,7 +194,7 @@ for recIdx = 1:n_recording
     end
 
     % Plot spectrum and time domain (optional)
-    % Scpe_sig_raw.spectrum("displayname", 'Rx Spectrum', 'fignum', 10, 'normalizeTo0dB', 0);
+    Scpe_sig_raw.spectrum("displayname", 'Rx Spectrum', 'fignum', 10, 'normalizeTo0dB', 0);
     Scpe_sig_raw.plot("clear", 1, "displayname", 'Rx Signal', 'fignum', 11);
 
     %% Save Routine
@@ -154,7 +205,6 @@ for recIdx = 1:n_recording
     rx_raw_path = [filesep, current_folder, filesep, filename, '_rx_signal_raw'];
     save([savePath, rx_raw_path], "Scpe_sig_raw");
 
-
     % Check if loop_id is already created
     if isempty(loop_id)
         newLoop = db.tables.LoopControl;
@@ -166,7 +216,6 @@ for recIdx = 1:n_recording
         fprintf('LoopControl entry created: loop_id = %d\n', loop_id);
     end
 
-
     %% Append to Runs Table
     newRun = db.tables.Runs;
     newRun.run_id = NaN;
@@ -184,9 +233,12 @@ for recIdx = 1:n_recording
     %% Append to Configurations Table
     conf.configuration_id = NaN;
     conf.run_id = run_id;
+    conf.bitrate = conf.symbolrate .* floor(log2(6)*10)/10;
+    conf.pam_source = Pamsource;
     db.appendToTable('Configurations', conf);
 
     %% Append to Measurements Table
+    laser.getLaserInfo;
     meas = db.tables.Measurements;
     meas.measurement_id = NaN;
     meas.run_id = run_id;
@@ -204,12 +256,17 @@ for recIdx = 1:n_recording
 
     db.appendToTable('Measurements', meas);
 
-
     %% Submit DSP Routine
-
-    if subimt_DSP
-        [out, a] = submit_dsp(run_id, databasePath, database_name, savePath, ...
-            "parallel", parallel_dsp, "max_occurences", max_occurences);
+    if recIdx == 1
+        if subimt_DSP
+            [out, a] = submit_dsp(run_id, databasePath, database_name, savePath, ...
+                "parallel", parallel_dsp, "max_occurences", max_occurences);
+        end
+        if awg_upload_required
+            [out, a] = submit_dsp(run_id, databasePath, database_name, savePath, ...
+                "parallel", 0, "max_occurences", 1);
+        end
     end
+
 end
 

projects/ECOC_2025/plots_from_database/bias_vs_ber.m

@@ -2,7 +2,7 @@
 
 basePath = 'C:\Users\sioe\Documents\MATLAB\imdd_simulation\projects\ECOC_2025\';
 database_name = 'ecoc2025.db';
-database = DBHandler("pathToDB", [basePath, database_name]);
+database = DBHandler("pathToDB", [basePath, database_name],"type",'mysql');
 
 filterParams = database.tables;
 filterParams.Configurations = struct( ...

projects/ECOC_2025/plots_from_database/plot_mpi_trial.m

@@ -1,49 +1,58 @@
 
-% basePath = 'C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\';
-% database = DBHandler("pathToDB",[basePath,'silas_labor.db']);
-database = DBHandler("type",'mysql','dataBase','labor');
 
-filterParams = database.tables;
-filterParams.Runs.loop_id = 209;
-% filterParams.Configurations = struct( ...
-%     'symbolrate',      112e9, ... %[224,336,360,390,420,448]
-%     'fiber_length',     0, ...
-%     'db_mode',        '"no_db"', ...
-%     'interference_attenuation', [], ...
-%     'interference_path_length', 1000, ...
-%     'is_mpi',           1, ...
-%     'pam_level',        4, ...
-%     'wavelength',       1310, ...
-%     'precomp_amp',      [], ...
-%     'signal_attenuation', [], ...
-%     'v_awg',            [], ...
-%     'v_bias',           [] ...
-%     );
+% dsp_options.database_type   = 'mysql';
+% dsp_options.dataBase   = 'labor';
+% dsp_options.storage_path    = 'Z:\2025\ECOC Silas\ecoc_2025\';
+% database = DBHandler("dataBase", [dsp_options.dataBase], "type", dsp_options.database_type);
+% filterParams = database.tables;
+% filterParams.Runs.loop_id = 209;
+% % filterParams.Configurations = struct( ...
+% %     'symbolrate',      112e9, ... %[224,336,360,390,420,448]
+% %     'fiber_length',     0, ...
+% %     'db_mode',        '"no_db"', ...
+% %     'interference_attenuation', [], ...
+% %     'interference_path_length', 1000, ...
+% %     'is_mpi',           1, ...
+% %     'pam_level',        4, ...
+% %     'wavelength',       1310, ...
+% %     'precomp_amp',      [], ...
+% %     'signal_attenuation', [], ...
+% %     'v_awg',            [], ...
+% %     'v_bias',           [] ...
+% %     );
+% 
+% % if 1
+% %     % filterParams.EqualizerParameters.dc_buffer_len              = 1;
+% %     filterParams.EqualizerParameters.ffe_buffer_len             = 1;
+% %     filterParams.EqualizerParameters.smoothing_buffer_len       = 4096;
+% %     filterParams.EqualizerParameters.smoothing_buffer_update    = 224;
+% %     filterParams.EqualizerParameters.DCmu                       = 0;
+% % end
+% a = database.getTableFieldNames('Runs');
+% b = database.getTableFieldNames('Results');
+% c = database.getTableFieldNames('EqualizerParameters');
+% d = [a;b;c];
+% 
+% [dataTable,~] = database.queryDB(filterParams, d);
+% 
+% selectedFields = {'Configurations.run_id' 'Runs.loop_id' 'Runs.date_of_run' 'Runs.rx_raw_path'	'Runs.bitrate' 'Runs.v_bias' 'Runs.v_awg' 'Runs.precomp_amp'	'Runs.symbolrate'	'Runs.pam_level'...
+%     'Runs.db_mode'	'Runs.rop_attenuation'	'Runs.is_mpi'	'Runs.interference_attenuation' 'Runs.interference_path_length' 'Runs.signal_attenuation'	...
+%     'EqualizerParameters.equalizer_structure'	'EqualizerParameters.diff_precode' 'EqualizerParameters.eq_id' 'EqualizerParameters.dc_buffer_len' 'EqualizerParameters.ffe_buffer_len' 'EqualizerParameters.smoothing_buffer_len' 'EqualizerParameters.smoothing_buffer_update'  'EqualizerParameters.DCmu' 'Measurements.power_pd_in' ...
+%     'Measurements.power_mpi_interference'	'Measurements.power_mpi_signal'	'Results.BER' 'Results.BER_precoded' 'Results.EVM'	'Results.SNR'	'Results.GMI'	'Results.Alpha' 'Results.date_of_processing'};
 
-% if 1
-%     % filterParams.EqualizerParameters.dc_buffer_len              = 1;
-%     filterParams.EqualizerParameters.ffe_buffer_len             = 1;
-%     filterParams.EqualizerParameters.smoothing_buffer_len       = 4096;
-%     filterParams.EqualizerParameters.smoothing_buffer_update    = 224;
-%     filterParams.EqualizerParameters.DCmu                       = 0;
-% end
-a = database.getTableFieldNames('Runs');
-b = database.getTableFieldNames('Results');
-c = database.getTableFieldNames('EqualizerParameters');
-d = [a;b;c];
+db = DBHandler("type","mysql","dataBase",'labor');
 
-[dataTable,~] = database.queryDB(filterParams, d);
+fp = QueryFilter();
+% fp.where('mpi_superview', 'loop_id','EQUALS', 209);     
+fp.where('mpi_superview', 'symbolrate','EQUALS', 112e9); 
+fp.where('mpi_superview', 'pam_level','EQUALS', 4);  
+fn = [db.getTableFieldNames('mpi_superview')];
+[dataTable,sql_query] = db.queryDB(fp,fn);
 
-selectedFields = {'Configurations.run_id' 'Runs.loop_id' 'Runs.date_of_run' 'Runs.rx_raw_path'	'Runs.bitrate' 'Runs.v_bias' 'Runs.v_awg' 'Runs.precomp_amp'	'Runs.symbolrate'	'Runs.pam_level'...
-    'Runs.db_mode'	'Runs.rop_attenuation'	'Runs.is_mpi'	'Runs.interference_attenuation' 'Runs.interference_path_length' 'Runs.signal_attenuation'	...
-    'EqualizerParameters.equalizer_structure'	'EqualizerParameters.diff_precode' 'EqualizerParameters.eq_id' 'EqualizerParameters.dc_buffer_len' 'EqualizerParameters.ffe_buffer_len' 'EqualizerParameters.smoothing_buffer_len' 'EqualizerParameters.smoothing_buffer_update'  'EqualizerParameters.DCmu' 'Measurements.power_pd_in' ...
-    'Measurements.power_mpi_interference'	'Measurements.power_mpi_signal'	'Results.BER' 'Results.BER_precoded' 'Results.EVM'	'Results.SNR'	'Results.GMI'	'Results.Alpha' 'Results.date_of_processing'};
-
-[dataTable_raw,sql_query] = database.queryDB(filterParams, selectedFields);
 
 %%
 
-dataTable_clean = dataTable_raw;
+dataTable_clean = dataTable;
 dataTable_clean.SIR = -7 - round(dataTable_clean.power_mpi_interference);
 dataTable_clean.NGMI = dataTable_clean.GMI ./ log2(dataTable_clean.pam_level);
 dataTable_clean = cleanUpTable(dataTable_clean);
@@ -66,7 +75,7 @@ for int_len = [0,50,300,1000]
     mode = 4;
 
 
-    for mode = [1,4]
+    for mode = [1,2]
         
         hold on;
         dataTable = dataTable_clean;
@@ -124,7 +133,7 @@ for int_len = [0,50,300,1000]
             method = 'ideal dc tracking';
         end
 
-        dataTable(dataTable.eq_id==0,:) = [];
+        % dataTable(dataTable.eq_id==0,:) = [];
         dataTable(dataTable.equalizer_structure~=1,:) = [];
 
         % Modify values in 'interference_path_length' where the condition is met
@@ -182,6 +191,10 @@ for int_len = [0,50,300,1000]
         dataTableGrpd_min = groupIt(fixedVars, dataTable, @min);
         dataTableGrpd_max = groupIt(fixedVars, dataTable, @max);
 
+        % dataTableGrpd_mean(dataTableGrpd_mean.nRows<50,:) = [];
+        % dataTableGrpd_min(dataTableGrpd_min.nRows<50,:) = [];
+        % dataTableGrpd_max(dataTableGrpd_max.nRows<50,:) = [];
+
         % Create a new figure
 
         hold on
@@ -237,6 +250,8 @@ for int_len = [0,50,300,1000]
                 % Hide patch (shaded area) from legend
                 set(hp, 'HandleVisibility', 'off','LineStyle',':','LineWidth',0.5,'Marker','none');
             
+
+
                 % Fit a 4th-order polynomial to log10(BER)
                 p = polyfit(x_values, log10(y_mean), 3); % 4 is fitting order, adjust as needed
 
@@ -286,9 +301,10 @@ for int_len = [0,50,300,1000]
                     'LineWidth', 0.5, 'HandleVisibility', 'off', 'DisplayName', string(dispname));
 
                 pair_one = {'Run ID', dataTable.run_id(loopFiltSingle, :)};
-                pair_two = {'Rate', dataTable.bitrate(loopFiltSingle, :) * 1e-9};
+                pair_two = {'Baud', dataTable.symbolrate(loopFiltSingle, :) * 1e-9};
                 pair_three = {'PD in', round(dataTable.power_pd_in(loopFiltSingle, :), 2)};
-                addDatatips(sc, pair_one, pair_two, pair_three);
+                pair_four = {'#bits', round(dataTable.numBits(loopFiltSingle, :), 2)};
+                addDatatips(sc, pair_one, pair_two, pair_three,pair_four);
             end
         end
 

projects/ECOC_2025/sqlite_sequence.sql

@@ -0,0 +1,163 @@
+BEGIN TRANSACTION;
+CREATE TABLE IF NOT EXISTS "Configurations" (
+	"configuration_id"	INTEGER,
+	"run_id"	INTEGER,
+	"unique_elab_id"	TEXT,
+	"bitrate"	REAL,
+	"symbolrate"	REAL,
+	"pam_level"	INTEGER,
+	"db_mode"	TEXT,
+	"pulsef_alpha"	INTEGER,
+	"v_bias"	REAL,
+	"v_awg"	REAL,
+	"precomp_amp"	REAL,
+	"rop_attenuation"	REAL,
+	"wavelength"	REAL,
+	"laser_power"	REAL,
+	"fiber_length"	REAL,
+	"pd_in_desired"	REAL,
+	"is_mpi"	BIT,
+	"signal_attenuation"	REAL,
+	"interference_path_length"	REAL,
+	"interference_attenuation"	REAL,
+	"pam_source"	TEXT,
+	PRIMARY KEY("configuration_id" AUTOINCREMENT),
+	FOREIGN KEY("run_id") REFERENCES "Runs"("run_id")
+);
+CREATE TABLE IF NOT EXISTS "EqualizerParameters" (
+	"eq_id"	INTEGER,
+	"equalizer_structure"	REAL,
+	"M"	INTEGER,
+	"target_constellation"	TEXT,
+	"db_target"	INTEGER,
+	"diff_precode"	INTEGER,
+	"postFFE"	INTEGER,
+	"NpostFFE"	INTEGER,
+	"Ne1"	INTEGER,
+	"Ne2"	INTEGER,
+	"Ne3"	INTEGER,
+	"Nb1"	INTEGER,
+	"Nb2"	INTEGER,
+	"Nb3"	INTEGER,
+	"K"	INTEGER,
+	"DCmu"	REAL,
+	"ideal_dfe"	INTEGER,
+	"training_length"	INTEGER,
+	"training_loops"	INTEGER,
+	"TRmu1"	REAL,
+	"TRmu2"	REAL,
+	"TRmu3"	REAL,
+	"TRmuDFE"	REAL,
+	"dd_loops"	INTEGER,
+	"DDmu1"	REAL,
+	"DDmu2"	REAL,
+	"DDmu3"	REAL,
+	"DDmuDFE"	REAL,
+	"MLSE_mode"	TEXT,
+	"MLSE_trellis_states"	TEXT,
+	"comment"	TEXT,
+	"config_hash"	TEXT,
+	UNIQUE("config_hash"),
+	PRIMARY KEY("eq_id" AUTOINCREMENT)
+);
+CREATE TABLE IF NOT EXISTS "Measurements" (
+	"measurement_id"	INTEGER,
+	"run_id"	INTEGER,
+	"power_laser"	REAL,
+	"power_rop"	REAL,
+	"power_pd_in"	REAL,
+	"power_mpi_interference"	REAL,
+	"power_mpi_signal"	REAL,
+	"voa_class"	TEXT,
+	"pdfa_class"	TEXT,
+	"laser_class"	TEXT,
+	PRIMARY KEY("measurement_id" AUTOINCREMENT),
+	FOREIGN KEY("run_id") REFERENCES "Runs"("run_id")
+);
+CREATE TABLE IF NOT EXISTS "Results" (
+	"result_id"	INTEGER,
+	"run_id"	INTEGER,
+	"eqParam_id"	INTEGER,
+	"date_of_processing"	DATETIME DEFAULT (datetime('now', 'localtime')),
+	"numBits"	INTEGER,
+	"numBitErr"	INTEGER,
+	"BER"	REAL,
+	"numBitErr_precoded"	REAL,
+	"BER_precoded"	REAL,
+	"SNR"	REAL,
+	"SNR_level"	TEXT,
+	"GMI"	REAL,
+	"AIR"	REAL,
+	"EVM"	REAL,
+	"EVM_level"	TEXT,
+	"Alpha"	REAL,
+	"result_hash"	TEXT UNIQUE,
+	"MLSE_dir"	INTEGER,
+	PRIMARY KEY("result_id" AUTOINCREMENT),
+	FOREIGN KEY("eqParam_id") REFERENCES "EqualizerParameters"("eq_id"),
+	FOREIGN KEY("run_id") REFERENCES "Runs"("run_id")
+);
+CREATE TABLE IF NOT EXISTS "Runs" (
+	"run_id"	INTEGER,
+	"date_of_run"	DATETIME DEFAULT (datetime('now', 'localtime')),
+	"tx_bits_path"	TEXT,
+	"tx_symbols_path"	TEXT,
+	"rx_sync_path"	TEXT,
+	"rx_raw_path"	TEXT,
+	"filename"	TEXT,
+	"tx_signal_path"	TEXT,
+	PRIMARY KEY("run_id" AUTOINCREMENT)
+);
+CREATE VIEW "View_ResultOverview" AS
+SELECT
+    -- Run info
+    Runs.run_id,
+    Runs.date_of_run,
+	
+    Results.BER,
+    Results.SNR,
+    Results.GMI,
+    Results.AIR,
+    Results.EVM,
+    Results.Alpha,
+	
+    -- Configurations
+    Configurations.symbolrate,
+    Configurations.pam_level,
+    Configurations.db_mode,
+    Configurations.pulsef_alpha,
+    Configurations.v_bias,
+    Configurations.v_awg,
+    Configurations.precomp_amp,
+    Configurations.is_mpi,
+	Configurations.signal_attenuation,
+    Configurations.interference_path_length,
+    Configurations.interference_attenuation,
+
+    -- Measurement data
+    Measurements.power_laser,
+    Measurements.power_rop,
+    Measurements.power_pd_in,
+    Measurements.power_mpi_interference,
+    Measurements.power_mpi_signal,
+
+    -- Equalizer parameters
+    EqualizerParameters.equalizer_structure,
+    EqualizerParameters.db_target,
+    EqualizerParameters.diff_precode
+
+
+FROM Results
+
+-- Join related tables
+LEFT JOIN Runs ON Results.run_id = Runs.run_id
+LEFT JOIN Configurations ON Configurations.run_id = Runs.run_id
+LEFT JOIN Measurements ON Measurements.run_id = Runs.run_id
+LEFT JOIN EqualizerParameters ON Results.eqParam_id = EqualizerParameters.eq_id;
+CREATE INDEX IF NOT EXISTS "idx_run_id_on_Configurations" ON "Configurations" (
+	"run_id"
+);
+CREATE INDEX IF NOT EXISTS "idx_run_id_on_Measurements" ON "Measurements" (
+	"run_id"
+);
+COMMIT;

projects/ECOC_2025/submit_dsp.m

@@ -19,7 +19,7 @@ if options.parallel
     % Check if a pool exists
     pool = gcp('nocreate');
     if isempty(pool)
-        parpool;
+        parpool(4);
     end
 
     % Submit the DSP function asynchronously

projects/ECOC_2025/theory/analytic_mpi_evaluation.m

@@ -1,13 +1,13 @@
 % This script is used to evaluate Fig. 1b) in the paper "Adaptive Removal of Multipath Interference in Short Reach 112 GBd PAM-4 IM/DD Systems"
 
 %% Parameters
-df       = 1e6;%150e3;        % Laser linewidth [Hz]
+df       = 1e6;        % Laser linewidth [Hz]
 SIR_dB   = 20;                % Interference attenuation [dB]
 alpha    = 10^(-SIR_dB/20);   % Interference attenuation [linear]
 n_fiber  = 1.467;             % Refractive index
 c        = physconst('lightspeed'); % [m/s]
 
-L        = linspace(0,400,40); % Interference delay [m]  
+L        = linspace(0,250,50); % Interference delay [m]  
 tau      = n_fiber./c.*L;       % Interference time (= tau) [s]
 
 tau_c = 1/(pi*df);              % laser coherence time [s]
@@ -22,7 +22,7 @@ N                = round(Tsim*fs);   % number of samples for each realization
 max_delay_samples  = round(max(tau)*fs); % largest delay that is evaluated (based on max. Interference delay)
 phase_noise_std   = sqrt(2*pi*df/fs); % standard dev. phase noise
 
-num_realizations = 10;             % number of parallel runs
+num_realizations = 50;             % number of parallel runs
 monte_carlo_variance = zeros(num_realizations, length(L));
 parfor r = 1:num_realizations
 
@@ -48,7 +48,9 @@ avg_of_mc_variances = mean(monte_carlo_variance, 1);
 std_of_mc_variances = std(monte_carlo_variance, 0, 1);
 
 %% Analytic variance
-analytic_variance = 2*alpha^2 * (1 - exp(-2*pi*df.*tau)).^2;
+L_        = linspace(0,250,500); % Interference delay [m]  
+tau_      = n_fiber./c.*L_;
+analytic_variance = 2*alpha^2 * (1 - exp(-2*pi*df.*tau_)).^2;
 
 %% Plot 
 cols =     [0.3467    0.5360    0.6907
@@ -62,20 +64,21 @@ hold on;
 plot(L, avg_of_mc_variances, 'LineWidth',2, 'DisplayName','Simulation','Color',cols(1,:),'LineStyle','-');
 errorbar(L, avg_of_mc_variances,std_of_mc_variances, 'LineWidth',0.7,'LineStyle','none', 'DisplayName','Simulation','Color',cols(1,:),'HandleVisibility','off');
 
-plot(L, analytic_variance, 'LineWidth',2, 'DisplayName','Analytic','Color',cols(2,:),'LineStyle','-');
+plot(L_, analytic_variance, 'LineWidth',2, 'DisplayName','Analytic','Color',cols(2,:),'LineStyle','-');
 xticks(coherence_length_multiples.*L_c);
-xticklabels(round(coherence_length_multiples.*L_c));
+xticklabels(round(coherence_length_multiples.*L_c,1));
 
 norm_to_coherence_len = 1;
 if norm_to_coherence_len
     xticklabels(coherence_length_multiples);
-    xlabel('$\tau_c$', 'FontSize',12);
+    xlabel('$n \cdot L_c$', 'FontSize',12);
+else
+    xlabel('Interference Delay [m]', 'FontSize',12);
 end
 
 xline(L_c.*coherence_length_multiples, 'LineWidth',1.5, 'DisplayName','Coh. Length','HandleVisibility','off','Color',[0.7,0.7,0.7],'LineStyle','-');
 xlim([0,L(end)]);
 yline(var_sat, '-.k','LineWidth',1.5, 'DisplayName','Saturation: 2$\alpha ^2$');
-xlabel('Interference Delay [m]', 'FontSize',12);
 grid on;
 ylabel('Intensity Variance', 'FontSize',12);
 title(sprintf('MPI Variance; %d MHz; SIR: %d dB',df.*1e-6,SIR_dB), 'FontSize',14);

projects/ECOC_2025/theory/coherence_length_plot.m

@@ -1,32 +1,32 @@
 %% Parameters
-df       = linspace(100e3,50e6,10000);   % Laser FWHM linewidth [Hz]
+df       = linspace(1,50e6,10000);   % Laser FWHM linewidth [Hz]
 n_fiber  = 1.467;                      % Fiber group index
 c        = 3e8;                        % Speed of light [m/s]
 
 % Compute coherence length (1/e of mean-fringe decay)
 tau_c = 1./(pi*df);
-L_c   = (c/n_fiber) .* tau_c;          % Coherence length [m]
+L_c   = (c.* tau_c/n_fiber) ;          % Coherence length [m]
 
 %% Plot
 figure('Color','w'); 
 loglog(df/1e6, L_c, 'LineWidth',2,'LineStyle','-'); % linewidth in MHz
-xticks([0.1, 1, 10, 50]);
-yticks([1, 10, 100, 1000]);
-yticklabels({'1','10','100','1000'})
+% xticks([0.1, 1, 10, 50]);
+% yticks([1, 10, 100, 1000]);
+% yticklabels({'1','10','100','1000'})
 grid on; box on;
-xlabel('Laser linewidth [MHz]','FontSize',12,'Interpreter','none');
-ylabel('Coherence length [m]','FontSize',12,'Interpreter','none');
-title('Coherence Length vs. Laser Linewidth','FontSize',14,'Interpreter','none');
+xlabel('Laser linewidth [MHz]','FontSize',12,'Interpreter','latex');
+ylabel('Coherence length [m]','FontSize',12,'Interpreter','latex');
+title('Coherence Length vs. Laser Linewidth','FontSize',14,'Interpreter','latex');
 
 %% Annotate some key points
-hold on;
-freqs = [150e3, 1e6, 10e6, 50e6]; % [Hz]
-for f = freqs
-    x = f/1e6;
-    y = (c/n_fiber) * (1/(pi*f));
-    scatter(x,y,'Marker','x','LineWidth',1,'MarkerEdgeColor','black');
-
-    text(x*1.1,y, sprintf('%.2f MHz', f/1e6), ...
-        'FontSize',10,'HorizontalAlignment','left');
-
-end
+% hold on;
+% freqs = [150e3, 1e6, 10e6, 50e6]; % [Hz]
+% for f = freqs
+%     x = f/1e6;
+%     y = (c/n_fiber) * (1/(pi*f));
+%     scatter(x,y,'Marker','x','LineWidth',1,'MarkerEdgeColor','black');
+% 
+%     text(x*1.1,y, sprintf('%.2f MHz', f/1e6), ...
+%         'FontSize',10,'HorizontalAlignment','left');
+% 
+% end

projects/HighSpeedExperiment_2024/Auswertung_JLT/ber_vs_dispersion.m

@@ -20,7 +20,7 @@ fp.where('Runs', 'rop_attenuation','EQUALS', 0);
 
 
 fields =  db.getTableFieldNames('power_state_info');
-fields = [fields; db.getTableFieldNames('dashboard_ungrouped')];
+fields = [fields; db.getTableFieldNames('dashboard_ungrouped_new')];
 [dataTable,~] = db.queryDB(fp, fields);
 
 eqstructures    = unique(dataTable.equalizer_structure);

projects/HighSpeedExperiment_2024/Auswertung_JLT/ber_vs_rate.m

@@ -5,7 +5,7 @@ db = DBHandler("dataBase", [dataBase], "type", database_type);
 
 fp = QueryFilter();
 % fp.where('Runs', 'run_id','EQUALS', 987);
-M = 8;
+M = 6;
 fp.where('Runs', 'pam_level','EQUALS', M);
 % fp.where('Runs', 'bitrate','LESS_THAN', 310e9);
 fp.where('Runs', 'fiber_length','EQUALS', 2);
@@ -13,12 +13,12 @@ fp.where('Runs', 'is_mpi','EQUALS', 0);
 % fp.where('Runs', 'interference_path_length','EQUALS', 1000);
 % fp.where('Runs', 'loop_id','GREATER_THAN', 11);
 % fp.where('Runs', 'sir','EQUALS',18);
-fp.where('Runs', 'wavelength','EQUALS', 1293);
+fp.where('Runs', 'wavelength','EQUALS', 1310);
 % fp.where('Runs', 'db_mode','EQUALS', 0);
 fp.where('Runs', 'rop_attenuation','EQUALS', 0);
 
 fields =  db.getTableFieldNames('power_state_info');
-fields = [fields; db.getTableFieldNames('dashboard_ungrouped_new')];
+fields = [fields; db.getTableFieldNames('dashboard_ungrouped_aug_nov_2025')];
 [dataTable,~] = db.queryDB(fp, fields);
 
 eqstructures = unique(dataTable.equalizer_structure);
@@ -29,7 +29,7 @@ show_bitrate = true;
 figure(5);
 hold on
 
-for eqs = [equalizer_structure.vnle, equalizer_structure.vnle_pf_mlse]
+for eqs = [equalizer_structure.vnle]
     
     % figure('Name',string([char(eqs),'']));
     % hold on
@@ -148,13 +148,14 @@ for eqs = [equalizer_structure.vnle, equalizer_structure.vnle_pf_mlse]
         
         xticks(xraw);
         if show_bitrate
-            xticks(200:25:500);
-            xlim([350 500]);
+        %     xticks(200:25:500);
+        %     xlim([350 500]);
+            xlim([min(xraw), max(xraw)]);
         else
             xlim([min(xraw), max(xraw)]);
         end
 
-        ylim([5e-4, 0.3]);
+        ylim([1e-4, 0.5]);
 
     end
     yline([2.2e-4, 4.85e-3, 2e-2],'LineWidth',1,'LineStyle','--','HandleVisibility','off');

projects/HighSpeedExperiment_2024/Auswertung_JLT/ber_vs_rate_only_best_configs.m

@@ -17,7 +17,7 @@ fp.where('Runs', 'wavelength','EQUALS', 1310);
 % fp.where('Runs', 'db_mode','EQUALS', 1);
 fp.where('Runs', 'rop_attenuation','EQUALS', 0);
 
-[dataTable,~] = db.queryDB(fp, db.getTableFieldNames('dashboard'));
+[dataTable,~] = db.queryDB(fp, db.getTableFieldNames('dashboard_ungrouped_after_nov_2025'));
 
 eqstructures = unique(dataTable.equalizer_structure);
 

projects/HighSpeedExperiment_2024/Auswertung_JLT/final/Copy_of_FIGURE_ROP.m

@@ -0,0 +1,146 @@
+%% ============================================================
+%  SETTINGS
+% ============================================================
+database_type = 'mysql';
+db = DBHandler("dataBase", "labor_highspeed", "type", database_type);
+
+fiberL  = 1;        % km
+wlen    = 1310;     % nm
+bit    = 300e9;    % example (adjust if needed)
+max_pd  = 7;        % ROP limit (same as before)
+
+PAM_list = [4 6 8];     % formats to compare
+
+% Colors for PAM formats
+colors = {clr.Paired.red, clr.Paired.green, clr.Paired.blue};
+
+% Best DSP selection:
+bestDSP = struct;
+bestDSP = struct;
+bestDSP.P4 = equalizer_structure.vnle_db_mlse;    % PAM-4
+bestDSP.P6 = equalizer_structure.vnle;    % PAM-6
+bestDSP.P8 = equalizer_structure.vnle;    % PAM-8
+
+
+
+%% ============================================================
+%  LOOP over PAM formats — extract data
+% ============================================================
+results = struct;
+
+for pi = 1:numel(PAM_list)
+    M = PAM_list(pi);
+    eq = bestDSP.(sprintf('P%d', M));
+
+    % ---- DB FILTER ----
+    fp = QueryFilter();
+    fp.where('Runs','pam_level','EQUALS',M);
+    fp.where('Runs','fiber_length','EQUALS',fiberL);
+    fp.where('Runs','wavelength','EQUALS',wlen);
+    fp.where('Runs','bitrate','EQUALS',bit);
+    fp.where('Runs','power_pd_in','LESS_THAN',max_pd);
+
+    fields = [
+        db.getTableFieldNames('power_state_info');
+        db.getTableFieldNames('dashboard_ungrouped_alltime')
+    ];
+    [T,~] = db.queryDB(fp, fields);
+
+    % ---- DSP OPTIONS ----
+    pre_emph = decide_preemph(M, eq);
+    precoded = decide_precoded(M, eq);
+
+    cfg = struct;
+    cfg.x_axis   = 'power_mzm';
+    cfg.y_axis   = 'BER';
+    cfg.agg      = 'min';
+    cfg.outlier  = 'none';
+    cfg.show_raw = false;
+
+    cfg.filters = struct( ...
+        'pam_level', M, ...
+        'fiber_length', fiberL, ...
+        'wavelength', wlen, ...
+        'bitrate', bit, ...
+        'is_mpi', 0, ...
+        'equalizer_structure', eq, ...
+        'pre_emph', pre_emph);
+
+    A = analyze_measurements_gpt(T, cfg);
+
+    results(pi).M   = M;
+    results(pi).x   = A.group{1}.x;
+    results(pi).color = colors{pi};
+
+    if precoded
+        results(pi).ber = A.group{1}.y_precoded;
+    else
+        results(pi).ber = A.group{1}.y;
+    end
+end
+
+
+%% ============================================================
+%  PLOT — all PAM formats in one ROP plot
+% ============================================================
+fig = figure(91);  hold on;
+
+lw = 2.2; ms = 7;
+
+for pi = 1:numel(results)
+    plot(results(pi).x, results(pi).ber, ...
+        '-o', ...
+        'LineWidth', lw, ...
+        'MarkerSize', ms, ...
+        'MarkerFaceColor', results(pi).color, ...
+        'Color', results(pi).color, ...
+        'DisplayName', sprintf('PAM-%d', results(pi).M));
+end
+
+set(gca,'YScale','log');
+grid minor;
+
+xlabel('ROP / Power (MZM) [dBm]');
+ylabel('BER');
+
+ylim([1e-4 2e-1]);
+
+legend('Location','best');
+title(sprintf('BER vs ROP — Best DSP (4,6,8) at %.0f GBd, λ=%d nm, %.0f km', ...
+    bit*1e-9, wlen, fiberL));
+
+beautifyBERplot();
+
+set(fig,'Position',1e3*[0.35 0.45 1.0 0.45]);
+
+%% ============================================================
+% DECISION LOGIC (INLINE FUNCTIONS)
+% ============================================================
+
+function pe = decide_preemph(M, eq)
+    % PRE-EMPH RULES:
+    switch M
+        case 4
+            if eq == equalizer_structure.vnle
+                pe = 1;     % PAM4: VNLE → pre-emph on
+            else
+                pe = 0;     % PAM4: all others → off
+            end
+        case {6,8}
+            pe = 1;         % PAM6/8: all → pre-emph on
+        otherwise
+            pe = 0;
+    end
+end
+
+
+function flag = decide_precoded(M, eq)
+    % PRE-CODE RULES:
+    if eq == equalizer_structure.vnle_db_mlse
+        flag = 1;                         % Always for DB-target
+    elseif eq == equalizer_structure.ml_mlse && M == 4
+        flag = 1;                         % PAM4: ML-based → precoded
+    else
+        flag = 0;
+    end
+end

projects/HighSpeedExperiment_2024/Auswertung_JLT/final/Copy_of_FIGURE_WAVELENGTH.m

@@ -0,0 +1,272 @@
+%% ============================================================
+%  LOAD DATA FOR PAM = 4,6,8
+% ============================================================
+database_type = 'mysql';
+db = DBHandler("dataBase", "labor_highspeed", "type", database_type);
+
+pam_levels = [4, 6, 8];      % three tiles
+bitrate_set = 360e9;
+fiberL = 10;
+
+fields = [
+    db.getTableFieldNames('power_state_info');
+    db.getTableFieldNames('dashboard_ungrouped_alltime')
+];
+
+%% ============================================================
+%  DEFINE DSP SCHEMES
+% ============================================================
+curves = struct;
+
+curves(1).name  = 'VNLE';
+curves(1).eq    = equalizer_structure.vnle;
+curves(1).color = clr.Paired.red;
+
+curves(2).name  = 'PF + MLSE';
+curves(2).eq    = equalizer_structure.vnle_pf_mlse;
+curves(2).color = clr.Paired.green;
+
+curves(3).name  = 'DB-target + MLSE';
+curves(3).eq    = equalizer_structure.vnle_db_mlse;
+curves(3).color = clr.Paired.blue;
+
+curves(4).name  = 'ML-based MLSE';
+curves(4).eq    = equalizer_structure.ml_mlse;
+curves(4).color = clr.Paired.purple;
+
+%% ============================================================
+%  ANALYSIS — NO PLOTTING
+%  results(p, k) → p: PAM index, k: DSP index
+% ============================================================
+results = struct;
+
+for p = 1:length(pam_levels)
+    M = pam_levels(p);
+
+    % --- Load DB rows for this PAM ---
+    fp = QueryFilter();
+    fp.where('Runs','pam_level','EQUALS', M);
+    fp.where('Runs','fiber_length','EQUALS', fiberL);
+    fp.where('Runs','bitrate','EQUALS', bitrate_set);
+    fp.where('Runs','is_mpi','EQUALS', 0);
+
+    [dataTable, ~] = db.queryDB(fp, fields);
+
+    for k = 1:numel(curves)
+
+        %% =====================================================
+        % DECIDE PRE-EMPHASIS AND PRECoded BER
+        % ======================================================
+        pre_emph     = decide_preemph(M, curves(k).eq);
+        use_precoded = decide_precoded(M, curves(k).eq);
+
+        %% ---- base config ----
+        cfg = struct;
+        cfg.x_axis = 'wavelength';
+        cfg.y_axis = 'BER';
+        cfg.agg    = 'min';
+        cfg.outlier = 'none';
+        % cfg.group_by = {'wavelength'};
+        cfg.show_raw = false;
+
+        cfg.filters = struct( ...
+            'pam_level', M, ...
+            'is_mpi', 0, ...
+            'bitrate', bitrate_set, ...
+            'fiber_length', fiberL, ...
+            'equalizer_structure', curves(k).eq, ...
+            'pre_emph', pre_emph);
+
+        %% ---- Run analysis ----
+        A = analyze_measurements_gpt(dataTable, cfg);
+
+        results(p,k).wavelength = A.group{1}.x;
+
+        %% ---- store BER variant ----
+        if use_precoded
+            results(p,k).ber = A.group{1}.y_precoded;
+        else
+            results(p,k).ber = A.group{1}.y;
+        end
+
+    end
+end
+
+
+%% ============================================================
+%  PLOT — 1×3 (PAM-4, PAM-6, PAM-8)
+% ============================================================
+fig = figure(9110); clf;
+tiledlayout(1,3,'TileSpacing','compact','Padding','compact');
+
+lw = 1.8;
+ms = 6;
+
+for p = 1:length(pam_levels)
+    nexttile; hold on;
+
+    for k = 1:numel(curves)
+        plot(results(p,k).wavelength, results(p,k).ber, ...
+            '-o', ...
+            'Color', curves(k).color, ...
+            'MarkerFaceColor', curves(k).color, ...
+            'MarkerSize', ms, ...
+            'LineWidth', lw, ...
+            'DisplayName', curves(k).name);
+    end
+
+    set(gca,'YScale','log');
+    grid on;
+    if p == 1
+    ylabel('BER');
+    else
+ylabel('');
+    end
+    xlabel('wavelength');
+    
+    ylim([4e-4, 0.1]);
+
+    beautifyBERplot();
+
+    yline([2.2e-4 4.85e-3 2e-2], ...
+        'LineWidth',1.1, 'Color',[0.2 0.2 0.2], ...
+        'LineStyle',':','HandleVisibility','off');
+
+    
+    if p == 1
+
+        x1 = 1290;
+        x2 = 1297;
+        x3 = 1300;
+        x4 = 1323;
+        x5 = 1325;
+        x6 = 1330;
+
+    elseif p == 2
+
+        x1 = 1290;
+        x2 = 1295;
+        x3 = 1300;
+        x4 = 1323.5;
+        x5 = 1325;
+        x6 = 1330;
+
+    elseif p == 3
+
+        x1 = 1290;
+        x2 = 1292;
+        x3 = 1298;
+        x4 = 1323;
+        x5 = 1327.5;
+        x6 = 1330;
+    end
+
+    % --- Get current y-limits ---
+    yl = ylim;
+
+    % --- LEFT AREA BELOW KP4 FEC ---
+    patch([x1 x2 x2 x1], [yl(1) yl(1) yl(2) yl(2)], ...
+        clr.Set1.red, ...          % RGB = red
+        'FaceAlpha', 0.1, ... % transparency 0.1
+        'EdgeColor', 'none');  % no border
+
+    % --- RIGHT AREA BELOW KP4 FEC ---
+    patch([x2 x3 x3 x2], [yl(1) yl(1) yl(2) yl(2)], ...
+        clr.Set1.blue, ...          % RGB = red
+        'FaceAlpha', 0.10, ... % transparency 0.1
+        'EdgeColor', 'none');  % no border
+
+    % --- LEFT AREA BELOW O-FEC ---
+    patch([x4 x5 x5 x4], [yl(1) yl(1) yl(2) yl(2)], ...
+        clr.Set1.blue, ...          % RGB = red
+        'FaceAlpha', 0.10, ... % transparency 0.1
+        'EdgeColor', 'none');  % no border
+
+    % --- RIGHT AREA BELOW O-FEC ---
+    patch([x5 x6 x6 x5], [yl(1) yl(1) yl(2) yl(2)], ...
+        clr.Set1.red, ...          % RGB = red
+        'FaceAlpha', 0.10, ... % transparency 0.1
+        'EdgeColor', 'none');  % no border
+
+    uistack(findobj(gca,'Type','patch'),'bottom'); % send the patch behind curves
+
+
+%  ax = gca;
+% axpos = ax.Position;  % [x y w h] normalized
+% xl = xlim;
+% yl = ylim;
+% 
+% % Convert axis coords → normalized figure coords
+% toNorm = @(x,y) [ ...
+%     axpos(1) + (x - xl(1)) / (xl(2)-xl(1)) * axpos(3), ...
+%     axpos(2) + (y - yl(1)) / (yl(2)-yl(1)) * axpos(4) ...
+% ];
+% 
+% % Choose vertical placement (10% above bottom of axis)
+% y_arrow = yl(1) * (yl(2)/yl(1))^0.10;   % works with log-scale axes
+% 
+% % === Arrow 1:  x3 <-> x4  ======================================
+% p1 = toNorm(x3, y_arrow);
+% p2 = toNorm(x4, y_arrow);
+% 
+% annotation('doublearrow', ...
+%     [p1(1) p2(1)], [p1(2) p2(2)], ...
+%     'Color', [0 0 0], 'LineWidth', 1.4);
+% 
+% % === Arrow 2:  x2 <-> x5  ======================================
+% p3 = toNorm(x2, y_arrow);
+% p4 = toNorm(x5, y_arrow);
+% 
+% annotation('doublearrow', ...
+%     [p3(1) p4(1)], [p3(2) p4(2)], ...
+%     'Color', [0 0 0], 'LineWidth', 1.4);
+
+end
+
+pos = 1e3.*[2.7770    1.2017    1.4000    0.3200];
+set(fig, 'Position', pos);
+
+%% === EXPORT ===
+outfile = 'C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\wavelength_analysis.tikz';
+matlab2tikz(outfile, ...
+    'width','\fwidth', ...
+    'height','\fheight', ...
+    'showInfo',false, ...
+    'extraAxisOptions',{ ...
+        'legend style={font=\footnotesize}', ...
+        'legend columns=1' ...
+    });
+
+
+
+%% ============================================================
+% DECISION LOGIC (INLINE FUNCTIONS)
+% ============================================================
+
+function pe = decide_preemph(M, eq)
+    % PRE-EMPH RULES:
+    switch M
+        case 4
+            if eq == equalizer_structure.vnle
+                pe = 1;     % PAM4: VNLE → pre-emph on
+            else
+                pe = 0;     % PAM4: all others → off
+            end
+        case {6,8}
+            pe = 1;         % PAM6/8: all → pre-emph on
+        otherwise
+            pe = 0;
+    end
+end
+
+
+function flag = decide_precoded(M, eq)
+    % PRE-CODE RULES:
+    if eq == equalizer_structure.vnle_db_mlse
+        flag = 1;                         % Always for DB-target
+    elseif eq == equalizer_structure.ml_mlse && M == 4
+        flag = 1;                         % PAM4: ML-based → precoded
+    else
+        flag = 0;
+    end
+end

projects/HighSpeedExperiment_2024/Auswertung_JLT/final/FIGURE_BER_3x4.m

@@ -0,0 +1,132 @@
+database_type   = 'mysql';
+dataBase        = 'labor_highspeed';
+db = DBHandler("dataBase", dataBase, "type", database_type);
+
+%% FILTER QUERY
+fp = QueryFilter();
+fp.where('Runs', 'fiber_length','EQUALS', 2);
+fp.where('Runs', 'wavelength','EQUALS', 1310);
+fp.where('Runs', 'rop_attenuation','EQUALS', 0);
+
+fields = db.getTableFieldNames('power_state_info');
+fields = [fields; db.getTableFieldNames('dashboard_ungrouped_alltime')];
+
+[dataTable,~] = db.queryDB(fp, fields);
+
+%% ---- CONFIG ----
+cfg = struct;
+cfg.x_axis = 'grossrate';
+cfg.y_axis = 'BER';
+cfg.y_scale = 'log';
+cfg.outlier = 'mad';
+cfg.show_raw = false;
+cfg.show_spread = 'none';
+cfg.agg = 'min';
+cfg.show_precoded = 1;
+cfg.fec_lines = [];
+
+cfg.plot = struct;
+cfg.plot.use_cbrewer2 = false;
+cfg.plot.lineWidth = 2.0;
+cfg.plot.errWidth  = 1.2;
+cfg.plot.scatterAlpha = 0.35;
+cfg.plot.legendLocation = 'best';
+cfg.plot.fecLineWidth = 2.4;
+cfg.plot.custom_colors_scatter = [];    % disabled
+
+%% ---- DSP DEFINITIONS ----
+DSP(1).name        = 'VNLE';
+DSP(1).eq          = equalizer_structure.vnle;
+DSP(1).color       = clr.Paired.red;
+DSP(1).lightcolor  = clr.Paired.lightred;
+
+DSP(2).name        = 'VNLE PF MLSE';
+DSP(2).eq          = equalizer_structure.vnle_pf_mlse;
+DSP(2).color       = clr.Paired.green;
+DSP(2).lightcolor  = clr.Paired.lightgreen;
+
+DSP(3).name        = 'VNLE DB MLSE';
+DSP(3).eq          = equalizer_structure.vnle_db_mlse;
+DSP(3).color       = clr.Paired.blue;
+DSP(3).lightcolor  = clr.Paired.lightblue;
+
+DSP(4).name        = 'ML MLSE';
+DSP(4).eq          = equalizer_structure.ml_mlse;
+DSP(4).color       = clr.Paired.purple;
+DSP(4).lightcolor  = clr.Paired.lightpurple;
+
+%% ---- GRID CONFIG ----
+rows = 3;          % PAM 4,6,8
+cols = 4;          % DSP schemes
+pam  = [4 6 8];
+
+cfg.figure_number = 46;
+fig = figure(cfg.figure_number); clf;
+
+t = tiledlayout(rows, cols, ...
+    'TileSpacing','compact', ...
+    'Padding','compact');
+
+cfg.group_by = {'equalizer_structure','pre_emph'};
+cfg.plot.use_cbrewer2 = false;
+
+%% ==== MAIN PLOT LOOP =====
+for r = 1:rows
+    Mlev = pam(r);
+
+    for c = 1:cols
+        ax = nexttile(t, (r-1)*cols + c);
+        cfg.ax = ax;
+
+        % ---- PRE-EMPH = 1 ----
+        cfg.filters = struct('is_mpi',0,'pam_level',Mlev, ...
+                             'equalizer_structure',DSP(c).eq, ...
+                             'pre_emph',1);
+        cfg.plot.custom_colors     = DSP(c).lightcolor;
+        cfg.plot.custom_linetypes  = {'-'};
+        [~, M1] = plot_measurements_gpt(dataTable, cfg);
+
+        % ---- PRE-EMPH = 0 ----
+        cfg.filters.pre_emph = 0;
+        cfg.plot.custom_colors     = DSP(c).color;
+        cfg.plot.custom_linetypes  = {'-'};
+        [~, M0] = plot_measurements_gpt(dataTable, cfg);
+
+        % Axis limits
+        if Mlev == 4
+            ylim([1e-5 0.3]);
+        elseif Mlev == 6
+            ylim([6e-4 0.1]);
+        elseif Mlev == 8
+            ylim([9e-4 0.1]);
+        end
+
+        % ---- FEC lines ----
+        yline([2.2e-4 4.85e-3 2e-2], ...
+            'LineWidth',1.1, 'Color',[0.2 0.2 0.2], ...
+            'LineStyle',':','HandleVisibility','off');
+
+        beautifyBERplot;
+
+        % ---- Remove redundant labels ----
+        if c > 1, ax.YLabel = []; end
+        if r < rows, ax.XLabel = []; end
+
+        grid(ax,'on'); box(ax,'on');
+    end
+end
+
+%% ---- FIXED FIGURE SIZE ----
+pos = 1e3.*[0.1070    0.5497    1.4113    0.6847];
+set(fig, 'Position', pos);
+
+% %% === EXPORT ===
+% outfile = 'C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\compare_pre_emphasis.tikz';
+% matlab2tikz(outfile, ...
+%     'width','\fwidth', ...
+%     'height','\fheight', ...
+%     'showInfo',false, ...
+%     'extraAxisOptions',{ ...
+%         'legend style={font=\footnotesize}', ...
+%         'legend columns=1' ...
+%     });

projects/HighSpeedExperiment_2024/Auswertung_JLT/final/FIGURE_EYES.m

@@ -0,0 +1,257 @@
+dsp_options.storage_path    = 'Z:\2024\sioe_labor\';
+dsp_options.max_occurences = 1;
+database    = DBHandler("dataBase", 'labor_highspeed', "type",  'mysql' );
+
+rate = [300e9];
+cols = cbrewer2('BuPu',25);
+cols = [cols(end-10:2:end,:)];
+cols = cbrewer2('Set1',6);
+
+fignum = 200;
+fig=figure(fignum);clf;
+
+dbmode = 0;
+
+
+% 1 - PAM 4 with preemphasis
+fp = QueryFilter();
+M = 6;
+fp.where('Runs', 'pam_level','EQUALS', M);
+fp.where('Runs', 'bitrate','EQUALS', rate);%360,390
+fp.where('Runs', 'fiber_length','EQUALS', 2);
+fp.where('Runs', 'wavelength','EQUALS', 1310);
+fp.where('Runs', 'db_mode','EQUALS', dbmode);
+fp.where('Runs', 'rop_attenuation','EQUAL', 0);
+
+[dataTable,~] = db.queryDB(fp, database.getTableFieldNames('Runs'));
+
+dataTable   = queryRunid(dataTable.run_id, database);
+fsym        = dataTable.symbolrate;
+M           = double(dataTable.pam_level);
+duob_mode   = db_mode(strrep(dataTable.db_mode,'"',''));
+
+% Load and Sync signal data from DB
+[Tx_bits, Symbols, Scpe_cell, ~] = loadAndSyncSignalDataFromDb(dataTable, dsp_options);
+
+% Preprocess signal
+Scpe_sig = preprocessSignal(Scpe_cell{1}, Symbols, fsym);
+
+Scpe_sig.eye(fsym,M,"fignum",M*10);
+
+%% === EXPORT TO TIKZ ===
+% outfile = ['C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\eye_pam_',num2str(M),'.tikz'];
+% outfile = ['C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\vnle_optimization.tikz'];
+% matlab2tikz(outfile, ...
+%     'width','\fwidth', ...
+%     'height','\fheight', ...
+%     'showInfo',false, ...
+%     'extraAxisOptions',{ ...
+%         'legend style={font=\footnotesize}', ...
+%         'legend columns=1' ...
+%     } );
+
+%%
+
+if duob_mode == db_mode.no_db && M == 6 %only for PAM-6 and no duobinary precoding, otherwise leads to false sequence estimation
+    trellexlusion = 1;
+else
+    trellexlusion = 0;
+end
+mlse_db_ = MLSE("DIR",[1,1],"duobinary_output",0,"M",M,"trellis_states",PAMmapper(M,0).levels,'scale_mode',2,'trellis_exclusion',trellexlusion,'trellis_state_mode',3);
+len_tr      = 4096*2;
+
+ffe_order   = [50, 5, 5];
+dfe_order   = [0, 0, 0];
+pf_ncoeffs  = 1;
+mu_ffe      = [0.0001, 0.0008, 0.001];
+mu_dfe      = 0.0004;
+mu_dc       = 0.005;
+dc_buffer_len = 1;
+
+mu_tr = 0;
+mu_dd = 0.05;
+adaption= 1;
+use_dd_mode = 1;
+ffe_order   = [50, 5, 5];
+eq_    = EQ("Ne",ffe_order,"Nb",dfe_order,"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
+
+dbt_results = duobinary_target(eq_, mlse_db_, M, Scpe_sig, Symbols, Tx_bits, ...
+    "precode_mode", duob_mode, ...
+    'showAnalysis', 1,...
+    "postFFE", []);
+
+%% === FINAL FIGURE SIZE ===
+
+% Existing figure numbers
+figEye   = 249;
+figConst = 341;
+
+% Find axes in the source figures
+srcAxEye   = findobj(figEye, 'Type', 'axes');
+srcAxConst = findobj(figConst, 'Type', 'axes');
+
+% Create new combined figure
+figCombined = figure;
+t = tiledlayout(figCombined, 1, 2);
+t.TileSpacing = 'compact';
+t.Padding = 'compact';
+
+% ------------------------------------------------------------
+% LEFT TILE: EYE DIAGRAM
+% ------------------------------------------------------------
+ax1 = nexttile(t, 1);
+hold(ax1, 'on')
+
+% Copy children (images, lines, patches, hist objects, etc.)
+copyobj(srcAxEye.Children, ax1);
+
+% Copy labels and title
+ax1.XLabel.String = srcAxEye.XLabel.String;
+ax1.YLabel.String = srcAxEye.YLabel.String;
+ax1.Title.String  = srcAxEye.Title.String;
+
+% Copy axis limits
+ax1.XLim = srcAxEye.XLim;
+ax1.YLim = srcAxEye.YLim;
+ax1.YDir = srcAxEye.YDir;
+
+% Copy ticks + labels EXACTLY (including remapped/scaled ones)
+ax1.XTick      = srcAxEye.XTick;
+ax1.XTickLabel = srcAxEye.XTickLabel;
+ax1.YTick      = srcAxEye.YTick;
+ax1.YTickLabel = srcAxEye.YTickLabel;
+
+% Copy colormap + clim (important for density eye)
+colormap(ax1, colormap(srcAxEye.Parent)); 
+ax1.CLim = srcAxEye.CLim;
+
+% Copy any style props that matter
+ax1.TickDir     = srcAxEye.TickDir;
+ax1.TickLength  = srcAxEye.TickLength;
+ax1.FontSize    = srcAxEye.FontSize;
+ax1.Box         = srcAxEye.Box;
+
+grid(ax1,'on');
+
+
+% ------------------------------------------------------------
+% RIGHT TILE: CONSTELLATION HISTOGRAM
+% ------------------------------------------------------------
+ax2 = nexttile(t, 2);
+hold(ax2, 'on')
+
+copyobj(srcAxConst.Children, ax2);
+
+% Copy labels and title
+ax2.XLabel.String = srcAxConst.XLabel.String;
+ax2.YLabel.String = srcAxConst.YLabel.String;
+ax2.Title.String  = srcAxConst.Title.String;
+
+% The histogram uses the same y-axis as the eye
+% Extract mapping from eye
+rawTicks      = ax1.YTick;
+rawLabelsCell = ax1.YTickLabel;
+trueVoltages  = str2double(rawLabelsCell);
+
+% Apply true voltages to the histogram axis
+ax2.XTick      = flip(trueVoltages);
+ax2.XTickLabel = flip(rawLabelsCell);
+
+% Set histogram y-limits to match the actual voltages
+ax2.XLim = [min(trueVoltages) max(trueVoltages)];
+
+% Ensure eye diagram prints the same (we *do not* touch ax1.YLim)
+ax1.XTickLabel = rawLabelsCell;
+
+
+% Copy colormap (your histogram uses same palette)
+colormap(ax2, colormap(srcAxConst.Parent));
+
+% Style properties
+ax2.TickDir     = srcAxConst.TickDir;
+ax2.TickLength  = srcAxConst.TickLength;
+ax2.FontSize    = srcAxConst.FontSize;
+ax2.Box         = srcAxConst.Box;
+
+grid(ax2,'on');
+
+% ============================================================
+% remove right y-axis completely
+% ============================================================
+ax2.XAxis.Visible = 'off';     % hides ticks + labels + axis line
+
+% BUT we still keep the YTick positions internally for alignment:
+% ax2.YTick = <values already set earlier> ;
+
+
+% ============================================================
+% minimize distance between the two plots
+% ============================================================
+t.TileSpacing = 'none';      % no space between tiles
+t.Padding     = 'none';      % no outer padding
+
+% Also reduce internal padding for each axis
+ax1.Position(3) = ax1.Position(3) + 0.02;   % widen eye a bit
+ax2.Position(1) = ax2.Position(1) - 0.02;   % pull histogram closer
+
+
+% Keep left axis grid visible
+ax2.YGrid = 'off';
+
+%
+%  =====================================================================
+% FINAL POLISHING: unified visual style
+% =======================================================================
+
+% --- unified font size ---
+FS = 12;
+set([ax1 ax2], 'FontSize', FS);
+
+% --- unified axis line width (outline stroke thickness) ---
+LW = 1.0;
+set([ax1 ax2], 'LineWidth', LW);
+
+% --- unified tick length ---
+TL = [.015 .015];
+set([ax1 ax2], 'TickLength', TL);
+
+% --- unified grid style ---
+set([ax1 ax2], 'XGrid', 'on', 'YGrid', 'on');
+set([ax1 ax2], 'GridLineStyle', '--');
+set([ax1 ax2], 'GridAlpha', 0.2);
+
+% --- remove right y-axis ticks and labels ---
+ax2.YAxis.Visible = 'off';
+
+% --- copy colormap + CLim from the eye to histogram (synchronize look) ---
+colormap(ax1, colormap(srcAxEye.Parent));
+colormap(ax2, colormap(srcAxEye.Parent));
+ax2.CLim = ax1.CLim;
+
+% --- minimal spacing between tiles ---
+t.TileSpacing = 'none';
+t.Padding     = 'none';
+
+
+% --- pull the panels together (touching boundary effect) ---
+pos1 = ax1.Position;
+pos2 = ax2.Position;
+
+% Shift histogram left until the outlines touch
+pos2(1) = pos1(1) + pos1(3) - 0.002;   % 0.002 = fine overlap control
+ax2.Position = pos2;
+
+% Expand histogram slightly, remove white band
+pos2 = ax2.Position;
+pos2(3) = pos2(3) + 0.01;
+ax2.Position = pos2;
+
+% Ensure the left plot stays correct after the move
+ax1.Position = pos1;
+
+% --- enforce same visible outline ---
+% For ax2, create a fake left spine (since YAxis is hidden)
+ax2.Box = 'on';   % keep outline but no ticks on the right
+ax1.Box = 'on';
+
+ax2.View = [90 -90];

projects/HighSpeedExperiment_2024/Auswertung_JLT/final/FIGURE_NGMI.m

@@ -0,0 +1,221 @@
+%% ============================================================
+%     GRID: NGMI, AIR, HD-NetRate, SD-NetRate  (1 × 4)
+% ============================================================
+
+db = DBHandler("dataBase","labor_highspeed","type","mysql");
+
+%% --- Base DB Filters (shared across all curves)
+fp = QueryFilter();
+fp.where('Runs','fiber_length','EQUALS', 2);
+fp.where('Runs','wavelength','EQUALS', 1310);
+fp.where('Runs','rop_attenuation','EQUALS', 0);
+fp.where('Runs','is_mpi','EQUALS', 0);
+
+fields = db.getTableFieldNames('dashboard_ungrouped_alltime');
+[dataTable,~] = db.queryDB(fp, fields);
+
+
+%% === Curve Definitions =======================================
+curves = struct;
+
+% PAM-8 — VNLE PF MLSE — no_emph = 1 — RED
+curves(1).pam   = 8;
+curves(1).eq    = equalizer_structure.vnle_pf_mlse;
+curves(1).pre   = 0;
+curves(1).color = clr.Paired.red;
+curves(1).mkr = 'o';
+
+% PAM-6 — VNLE PF MLSE — no_emph = 1 — BLUE
+curves(2).pam   = 6;
+curves(2).eq    = equalizer_structure.vnle_pf_mlse;
+curves(2).pre   = 1;
+curves(2).color = clr.Paired.blue;
+curves(2).mkr = 'square';
+
+% PAM-4 — VNLE DB MLSE — pre_emph = 0 — GREEN
+curves(3).pam   = 4;
+curves(3).eq    = equalizer_structure.vnle_db_mlse;
+curves(3).pre   = 0;
+curves(3).color = clr.Paired.green;
+curves(3).mkr = 'diamond';
+
+%% === Prepare Analysis Config ==================================
+base = struct;
+base.group_by = {'equalizer_structure','pre_emph'};
+base.x_axis   = 'symbolrate';
+base.outlier  = 'none';
+base.show_raw = false;
+base.filters  = struct;  % will be filled per curve
+
+
+%% === Precompute All Curves ====================================
+results = struct;
+
+for k = 1:numel(curves)
+
+    % --- BER ---
+    cfg = base;
+    cfg.y_axis = 'BER';
+    cfg.agg    = 'min';
+    cfg.filters = struct('pam_level', curves(k).pam, ...
+                         'equalizer_structure', curves(k).eq, ...
+                         'pre_emph', curves(k).pre);
+
+    A = analyze_measurements_gpt(dataTable, cfg);
+    cfg.x_axis = 'grossrate';
+    B = analyze_measurements_gpt(dataTable, cfg);
+
+    results(k).baudr = A.group{1}.x;
+    results(k).gross = B.group{1}.x;
+    
+    if curves(k).pam == 4
+        results(k).ber   = A.group{1}.y_precoded;
+    else
+        results(k).ber   = A.group{1}.y;
+    end
+
+    % --- NGMI ---
+    cfg.y_axis = 'NGMI';
+    cfg.agg    = 'max';
+    A = analyze_measurements_gpt(dataTable, cfg);
+    results(k).ngmi = A.group{1}.y;
+
+
+
+    % --- AIR ---
+    cfg.y_axis = 'AIR';
+    cfg.agg    = 'max';
+    A = analyze_measurements_gpt(dataTable, cfg);
+    results(k).air = A.group{1}.y;
+        results(k).air = results(k).ngmi .* results(k).gross;
+
+    % --- Net Rates ---
+    tp = TransmissionPerformance;
+    results(k).ndr = tp.calculateNetRate(results(k).gross, ...
+                                         'NGMI', results(k).ngmi, ...
+                                         'BER',  results(k).ber);
+end
+
+
+%% ============================================================
+%                 FIGURE: 1 × 4 GRID
+% ============================================================
+fig = figure(71); clf;
+t = tiledlayout(1,4, 'TileSpacing','compact', 'Padding','compact');
+
+lw = 1.0;
+
+% === NGMI vs Grossrate ===
+ax = nexttile(t,1);
+hold on;
+for k = 1:3
+    plot(results(k).baudr, results(k).ngmi, ...
+        'LineWidth', lw, ...
+        'Color', curves(k).color, ...
+        'MarkerSize', 1, ...
+        'MarkerFaceColor', curves(k).color,...
+        'Marker',curves(k).mkr);
+end
+ylabel('NGMI');
+xlabel('Baud rate [GBd]');
+xlim([100 210]);
+xticks(100:15:225);
+ylim([0.9, 1]);
+grid minor; box on;
+beautifyBERplot("logscale",0,"setmarkers",0);
+
+
+% === AIR vs Grossrate ===
+ax = nexttile(t,2);
+hold on;
+for k = 1:3
+    plot(results(k).baudr, results(k).air, ...
+        '-', 'LineWidth', lw, ...
+        'Color', curves(k).color, ...
+        'MarkerSize', 2, ...
+        'MarkerFaceColor', curves(k).color,'Marker',curves(k).mkr);
+end
+ylabel('AIR [Gb/s]');
+xlabel('Baud rate [GBd]');
+ylim([280 430]);
+yticks(280:30:440)
+xlim([100 210]);
+xticks(100:15:225);
+grid minor; box on;
+beautifyBERplot("logscale",0,"setmarkers",0);
+yline(400,'LineStyle','--');
+
+% === SD-FEC Net Rate ===
+ax = nexttile(t,3);
+hold on;
+for k = 1:3
+    plot(results(k).baudr, results(k).ndr.SDHD.NetRate, ...
+        'LineWidth', lw, ...
+        'Color', curves(k).color, ...
+        'MarkerSize', 2, ...
+        'MarkerFaceColor', curves(k).color,...
+        'Marker',curves(k).mkr);
+end
+ylabel('NDR [Gb/s]');
+xlabel('Baud rate [GBd]');
+ylim([280 430]);
+yticks(280:30:440)
+xlim([100 210]);
+xticks(100:15:225);
+grid minor; box on;
+beautifyBERplot("logscale",0,"setmarkers",0);
+yline(400,'LineStyle','--');
+
+% === HD-FEC Net Rate ===
+ax = nexttile(t,4);
+hold on;
+for k = 1:3
+    % plot(results(k).baudr, results(k).ndr.STAIR.NetRate, ...
+    %     '-', 'LineWidth', lw, ...
+    %     'Color', curves(k).color, ...
+    %     'MarkerSize', 4,'Marker','+', ...
+    %     'MarkerFaceColor', curves(k).color);
+
+        plot(results(k).baudr, results(k).ndr.O_FEC.NetRate, ...
+        ':', 'LineWidth', lw, ...
+        'Color', curves(k).color, ...
+        'MarkerSize', 2,...
+        'MarkerFaceColor', curves(k).color,...
+        'Marker',curves(k).mkr);
+
+            plot(results(k).baudr, results(k).ndr.KP4_hamming.NetRate, ...
+        '--', 'LineWidth', lw, ...
+        'Color', curves(k).color, ...
+        'MarkerSize', 2,'Marker','diamond', ...
+        'MarkerFaceColor', curves(k).color,...
+        'Marker',curves(k).mkr);
+end
+
+yline(400,'LineStyle','--');
+ylabel('');
+xlabel('Baud rate [GBd]');
+ylim([280 430]);
+yticks(280:30:440)
+xlim([100 210]);
+xticks(100:15:225);
+grid minor; box on;
+beautifyBERplot("logscale",0,"setmarkers",0);
+
+% === FINAL FIGURE SIZE ===
+pos = 1e3.*[0.7950    1.1150    1.4113    0.1900];
+set(fig, 'Position', pos);
+
+% % % %% === EXPORT ===
+outfile = 'C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\compare_ndr_v3.tikz';
+matlab2tikz(outfile, ...
+    'width','\fwidth', ...
+    'height','\fheight', ...
+    'showInfo',false, ...
+    'extraAxisOptions',{ ...
+        'legend style={font=\footnotesize}', ...
+        'legend columns=1' ...
+         'every axis/.append style={font=\scriptsize}',...
+         'minor grid style={line width=0.2pt, solid, color=black!10}',...
+         'grid style={line width=0.4pt, solid, color=black!20}',...
+        'grid style={dashed}',...
+    });

projects/HighSpeedExperiment_2024/Auswertung_JLT/final/FIGURE_NGMI_v2.m

@@ -0,0 +1,180 @@
+%% ============================================================
+%       GRID (1 × 4):
+%   1) NGMI overview (PAM4+PAM6+PAM8 superimposed)
+%   2) PAM-4 tile (AIR + SD-NDR + HD-NDR)
+%   3) PAM-6 tile
+%   4) PAM-8 tile
+% ============================================================
+
+db = DBHandler("dataBase","labor_highspeed","type","mysql");
+
+%% --- Base DB Filters (shared across all curves)
+fp = QueryFilter();
+fp.where('Runs','fiber_length','EQUALS', 2);
+fp.where('Runs','wavelength','EQUALS', 1310);
+fp.where('Runs','rop_attenuation','EQUALS', 0);
+fp.where('Runs','is_mpi','EQUALS', 0);
+
+fields = db.getTableFieldNames('dashboard_ungrouped_alltime');
+[dataTable,~] = db.queryDB(fp, fields);
+
+%% === CURVE DEFINITIONS =================================================
+curves = struct;
+
+curves(1).pam   = 8;
+curves(1).eq    = equalizer_structure.vnle_pf_mlse;
+curves(1).pre   = 0;
+curves(1).color = clr.Paired.red;
+
+curves(2).pam   = 6;
+curves(2).eq    = equalizer_structure.vnle_pf_mlse;
+curves(2).pre   = 1;
+curves(2).color = clr.Paired.blue;
+
+curves(3).pam   = 4;
+curves(3).eq    = equalizer_structure.vnle_db_mlse;
+curves(3).pre   = 0;
+curves(3).color = clr.Paired.green;
+
+% === ANALYSIS ENGINE (extract BER/NGMI/AIR/netrates) ===================
+base = struct;
+base.group_by = {'equalizer_structure','pre_emph'};
+base.x_axis   = 'grossrate';
+base.outlier  = 'none';
+base.show_raw = false;
+
+results = struct;
+
+for k = 1:numel(curves)
+
+    % ========== BER ==========
+    cfg = base;
+    cfg.y_axis = 'BER';
+    cfg.agg    = 'min';
+    
+    cfg.filters = struct('pam_level', curves(k).pam, ...
+                         'equalizer_structure', curves(k).eq, ...
+                         'pre_emph', curves(k).pre);
+    A = analyze_measurements_gpt(dataTable, cfg);
+
+    results(k).gross = A.group{1}.x;
+    if curves(k).pam == 4
+        results(k).ber   = A.group{1}.y_precoded;
+    else
+        results(k).ber   = A.group{1}.y;
+    end
+
+    % ========== NGMI ==========
+    cfg.y_axis = 'NGMI'; cfg.agg = 'max';
+    A = analyze_measurements_gpt(dataTable, cfg);
+    results(k).ngmi = A.group{1}.y;
+
+    % ========== AIR ==========
+    cfg.y_axis = 'AIR'; cfg.agg = 'max';
+    A = analyze_measurements_gpt(dataTable, cfg);
+    results(k).air = A.group{1}.y;
+
+    % ========== NET RATES ==========
+    tp = TransmissionPerformance;
+    results(k).ndr = tp.calculateNetRate(results(k).gross, ...
+                                         'NGMI', results(k).ngmi, ...
+                                         'BER',  results(k).ber);
+end
+
+
+% ============================================================
+%                      FIGURE
+% ============================================================
+fig = figure(3);
+t = tiledlayout(1,4,'TileSpacing','compact','Padding','compact');
+
+lw = 1.7;
+
+% =======================================================================
+% (1) NGMI OVERVIEW TILE   (all 3 curves)
+% =======================================================================
+ax = nexttile(t,1); hold on;
+
+for k = 1:3
+    plot(results(k).gross, results(k).ngmi, ...
+        '-o', 'Color', curves(k).color, ...
+        'LineWidth',lw,'MarkerSize',5, ...
+        'MarkerFaceColor',curves(k).color);
+end
+
+ylabel('NGMI');
+xlabel('Grossrate [Gb/s]');
+ylim([0.9 1]);   % your chosen limits
+xlim([300 480]);
+xticks(300:30:480)
+grid minor; box on;
+beautifyBERplot;
+
+% =======================================================================
+% (2–4) PAM-SPECIFIC TILES: AIR, SD-NDR, HD-NDR
+% =======================================================================
+
+pam_order = [4 6 8];     % left → right
+
+for ti = 1:3
+    pam_target = pam_order(ti);
+    ax = nexttile(t, 1+ti); hold on;
+
+    % find matching curve
+    for k = 1:3
+        if curves(k).pam ~= pam_target, continue; end
+
+        col = curves(k).color;
+
+        % AIR
+        plot(results(k).gross, results(k).air, ...
+            '-','Color',col,'LineWidth',lw,'Marker','*', ...
+            'MarkerSize',5,'MarkerFaceColor',col,'DisplayName','AIR');
+
+        % SD-based net rate
+        plot(results(k).gross, results(k).ndr.SDHD.NetRate, ...
+            '--','Color',col,'LineWidth',lw,'Marker','v', ...
+            'MarkerSize',5,'MarkerFaceColor',col,'DisplayName','SD+HD');
+
+        % HD-based net rate
+        plot(results(k).gross, results(k).ndr.STAIR.NetRate, ...
+            ':','Color',col,'LineWidth',lw,'Marker','x', ...
+            'MarkerSize',5,'MarkerFaceColor',col,'DisplayName','HD-FEC (Staircase)');
+
+        % HD-based net rate
+        plot(results(k).gross, results(k).ndr.O_FEC.NetRate, ...
+            'LineStyle','-.','Color',col,'LineWidth',lw,'Marker','+', ...
+            'MarkerSize',5,'MarkerFaceColor',col,'DisplayName','O-FEC');
+
+        % HD-based net rate
+        plot(results(k).gross, results(k).ndr.KP4_hamming.NetRate, ...
+            'LineStyle','-','Color',col,'LineWidth',lw,'Marker','x', ...
+            'MarkerSize',5,'MarkerFaceColor',col,'DisplayName','KP4+Hamming');
+    end
+
+    ylabel('NDR [Gb/s]');
+    xlabel('Grossrate [Gb/s]');
+    ylim([300 440]);   % your chosen limits
+    yticks(300:20:480)
+    xlim([300 480]); 
+    xticks(300:30:480)
+    grid minor; box on;
+    beautifyBERplot;
+    yline(400,'HandleVisibility','off');
+end
+
+% === FIX FIGURE SIZE FOR TIKZ ==========================================
+if 0
+pos = 1e3.*[0.3643    0.9943    1.4113    0.2120];
+set(fig,'Position',pos);
+
+% outfile = 'C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\compare_ndr.tikz';
+% matlab2tikz(outfile, ...
+%     'width','\fwidth', ...
+%     'height','\fheight', ...
+%     'showInfo',false, ...
+%     'extraAxisOptions',{ ...
+%         'legend style={font=\footnotesize}', ...
+%         'legend columns=1' ...
+%     });
+end

projects/HighSpeedExperiment_2024/Auswertung_JLT/final/FIGURE_ROP.m

@@ -0,0 +1,153 @@
+%% ============================================================
+%  LOAD DATA (PAM-4, sweep over ROP)
+% ============================================================
+database_type = 'mysql';
+db = DBHandler("dataBase", "labor_highspeed", "type", database_type);
+
+pam_level = 4;
+fiberL = 1;                      % 1 km
+wlen = 1310;
+baudrate = 360e9;
+
+fp = QueryFilter();
+fp.where('Runs','pam_level','EQUALS', pam_level);
+fp.where('Runs','fiber_length','EQUALS', fiberL);
+fp.where('Runs','wavelength','EQUALS', wlen);
+fp.where('Runs','bitrate','EQUALS', baudrate);
+fp.where('Runs','power_pd_in','LESS_THAN', 7);
+
+fields = [
+    db.getTableFieldNames('power_state_info');
+    db.getTableFieldNames('dashboard_ungrouped_alltime')
+];
+
+[dataTable,~] = db.queryDB(fp, fields);
+
+
+%% ============================================================
+%  DSP SCHEMES (Best combinations only)
+% ============================================================
+curves = struct;
+
+curves(1).name  = 'VNLE';
+curves(1).eq    = equalizer_structure.vnle;
+curves(1).color = clr.Paired.red;
+
+curves(2).name  = 'PF + MLSE';
+curves(2).eq    = equalizer_structure.vnle_pf_mlse;
+curves(2).color = clr.Paired.green;
+
+curves(3).name  = 'DB-target + MLSE';
+curves(3).eq    = equalizer_structure.vnle_db_mlse;
+curves(3).color = clr.Paired.blue;
+
+curves(4).name  = 'ML-based MLSE';
+curves(4).eq    = equalizer_structure.ml_mlse;
+curves(4).color = clr.Paired.purple;
+
+
+
+
+%% ============================================================
+%  ANALYSIS ENGINE (No plotting)
+% ============================================================
+results = struct;
+
+for k = 1:numel(curves)
+
+    pre_emph   = decide_preemph(pam_level,curves(k).eq);
+    precoded   = decide_precoded(pam_level,curves(k).eq);
+
+    cfg = struct;
+    cfg.x_axis   = 'power_mzm';      % ROP axis
+    cfg.y_axis   = 'BER';
+    cfg.agg      = 'min';
+    cfg.outlier  = 'none';
+    cfg.show_raw = false;
+
+    cfg.filters = struct( ...
+        'pam_level', pam_level, ...
+        'fiber_length', fiberL, ...
+        'wavelength', wlen, ...
+        'bitrate', baudrate, ...
+        'is_mpi', 0, ...
+        'equalizer_structure', curves(k).eq, ...
+        'pre_emph', pre_emph);
+
+    A = analyze_measurements_gpt(dataTable, cfg);
+
+    results(k).x = A.group{1}.x;
+    if precoded
+        results(k).ber = A.group{1}.y_precoded;
+    else
+        results(k).ber = A.group{1}.y;
+    end
+end
+
+
+%% ============================================================
+%  PLOT — BER vs ROP (Single Axis)
+% ============================================================
+fig = figure(); clf; hold on;
+
+lw = 2.0;
+ms = 7;
+
+for k = 1:numel(curves)
+    plot(results(k).x, results(k).ber, ...
+        '-o', ...
+        'Color', curves(k).color, ...
+        'MarkerFaceColor', curves(k).color, ...
+        'MarkerSize', ms, ...
+        'LineWidth', lw, ...
+        'DisplayName', curves(k).name);
+end
+
+set(gca,'YScale','log');
+grid on;
+
+xlabel('ROP / Power (MZM) [dBm]');
+ylabel('BER');
+
+ylim([1e-4 2e-1]);
+
+title(sprintf('BER vs ROP — PAM-%d, %.0f km, %.0f GBd, %.0f nm', ...
+      pam_level, fiberL, baudrate*1e-9, wlen));
+
+legend('Location','best');
+beautifyBERplot();
+
+pos = 1e3.*[0.2 0.6 1.3 0.4];
+set(fig, 'Position', pos);
+
+%% ============================================================
+% DECISION LOGIC (INLINE FUNCTIONS)
+% ============================================================
+
+function pe = decide_preemph(M, eq)
+    % PRE-EMPH RULES:
+    switch M
+        case 4
+            if eq == equalizer_structure.vnle
+                pe = 1;     % PAM4: VNLE → pre-emph on
+            else
+                pe = 0;     % PAM4: all others → off
+            end
+        case {6,8}
+            pe = 1;         % PAM6/8: all → pre-emph on
+        otherwise
+            pe = 0;
+    end
+end
+
+
+function flag = decide_precoded(M, eq)
+    % PRE-CODE RULES:
+    if eq == equalizer_structure.vnle_db_mlse
+        flag = 1;                         % Always for DB-target
+    elseif eq == equalizer_structure.ml_mlse && M == 4
+        flag = 1;                         % PAM4: ML-based → precoded
+    else
+        flag = 0;
+    end
+end

projects/HighSpeedExperiment_2024/Auswertung_JLT/final/FIGURE_Spectra.m

@@ -0,0 +1,182 @@
+dsp_options.storage_path    = 'Z:\2024\sioe_labor\';
+dsp_options.max_occurences = 1;
+database    = DBHandler("dataBase", 'labor_highspeed', "type",  'mysql' );
+
+rates = [300e9];
+cols = cbrewer2('BuPu',25);
+cols = [cols(end-10:2:end,:)];
+cols = cbrewer2('Set1',6);
+
+fignum = 200;
+fig=figure(fignum);clf;
+
+for dbmode = 0:1%length(rates)
+
+
+    if 0
+    rcalpha     = 0.05;
+    fsym = rates/2;
+    pulsef = 1;
+    Pform       = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rrc","pulselength",16,"alpha",rcalpha);
+
+    Pamsource = PAMsource(...
+        "fsym",fsym,"M",4,"order",18,"useprbs",0,...
+        "fs_out",fdac,...
+        "applyclipping",0,"clipfactor",1.2,...
+        "applypulseform",pulsef,"pulseformer",Pform,...
+        "randkey",20,...
+        "db_precode",dbmode,"db_encode",0,...
+        "mrds_code",0,"mrds_blocklength",512);
+
+    [Digi_sig,Symbols,Bits] = Pamsource.process();
+
+    Digi_sig = Digi_sig.normalize("mode","rms");
+
+    %%% 1) PLOT FULL RESPONSE SIGNAL
+    Digi_sig.spectrum("displayname","Full Response","fignum",fignum+dbmode,"normalizeToNyquist",0,"normalizeTo0dB",0,"color",[0.2,0.2,0.2],"linestyle",'-','addDCoffset',0,'normalizeToDC',1);
+
+    
+    %%% 2) PLOT PREEMPH. TX SIGNAL
+    if dbmode == 0
+        maxamp = -37;
+        precomp_est = ChannelFreqResp("Nacq",2048,"Navg",100,"Ncp",63,'f_ref',Digi_sig.fs);
+
+        precomp_path = "C:\Users\Silas\Documents\MATLAB\Datensätze\sioe_labor\precomp";
+        precomp_fn = "lab_high_speed";
+        Digi_sig_pre = precomp_est.precomp(Digi_sig,'maxampdb',maxamp,'loadPath',precomp_path,'fileName',precomp_fn);
+
+        Digi_sig_pre = Digi_sig_pre.resample("fs_out",fdac);
+
+        Digi_sig_pre= Digi_sig_pre.normalize("mode","rms");
+
+        Digi_sig_pre.spectrum("displayname","Strong Precomp","fignum",fignum+dbmode,"normalizeToNyquist",0,"normalizeTo0dB",0,"color",[0,0,0],"linestyle",'-.','addDCoffset',0,'normalizeToDC',1);
+    end
+
+    end
+
+    % 1 - PAM 4 with preemphasis
+    fp = QueryFilter();
+    M = 4;
+    fp.where('Runs', 'pam_level','EQUALS', M);     
+    fp.where('Runs', 'bitrate','EQUALS', rates);%360,390
+    fp.where('Runs', 'fiber_length','EQUALS', 10);   
+    fp.where('Runs', 'wavelength','EQUALS', 1322.7);  %1327.4       
+    fp.where('Runs', 'db_mode','EQUALS', dbmode);              
+    fp.where('Runs', 'rop_attenuation','EQUAL', 0);      
+    
+    [dataTable,~] = database.queryDB(fp, database.getTableFieldNames('Runs'));
+    
+    dataTable   = queryRunid(dataTable.run_id, database);
+    fsym        = dataTable.symbolrate;
+    M           = double(dataTable.pam_level);
+    
+    % Load and Sync signal data from DB
+    [Tx_bits, Symbols, Scpe_cell, ~] = loadAndSyncSignalDataFromDb(dataTable, dsp_options);
+    
+    % Preprocess signal
+    Scpe_sig = preprocessSignal(Scpe_cell{1}, Symbols, fsym);
+    Scpe_sig = Scpe_cell{1};
+
+    %%% 3) PLOT DB Tgt. SIGNAL
+    if 1
+        DB_Symbols = Duobinary().encode(Symbols);
+        DB_Symbols.spectrum("fignum",fignum+dbmode,"normalizeTo0dB",1,"displayname",'DB-Response','addDCoffset',0,'color',clr.Set1.blue,'normalizeToNyquist',0,'linestyle','--');
+    end
+
+    %%% 4) Plot RX Signal
+    Scpe_sig.spectrum("fignum",fignum+dbmode,"normalizeTo0dB",1,"displayname",'Rx','addDCoffset',1,'color',[0,0,0],'normalizeToNyquist',0,'linestyle',':');
+Scpe_sig.eye(fsym,M,"fignum",47,"displayname",' Eye of AVG Signal');
+    % xline(Symbols.fs/2.*1e-9,'Color',cols(r,:),'HandleVisibility','off');
+
+    average_signals = 1;
+    if average_signals
+        Scpe_sig_avg = Scpe_sig;
+        scope_mean = zeros(size(Scpe_cell{1}.signal));
+        for n=1:numel(Scpe_cell)
+            scope_mean = scope_mean + Scpe_cell{n}.signal;
+        end
+        scope_mean = scope_mean ./ n;
+        Scpe_sig_avg.signal = scope_mean;
+
+        Scpe_sig_avg.spectrum("displayname","Scope PSD","fignum",20,"normalizeTo0dB",1);
+        Scpe_sig_avg.plot("displayname","Scope raw signal","fignum",27,"clear",1);
+        Scpe_sig_avg.eye(fsym,M,"fignum",48,"displayname",' Eye of AVG Signal');
+    end
+
+
+    fig = figure(fignum+dbmode);
+    if dbmode == 0
+        ylim([-22,12]);
+    else
+        ylim([-22,2]);
+    end
+    xlim([0,105]);
+    xticks(-100:20:100);
+    yticks(-20:10:10);
+
+    beautifyBERplot("logscale",0,"setmarkers",0)
+    pos = [100.3333  991.6667  358.0000  192.6667];
+    set(fig, 'Position', pos);
+
+    %%%%%%%%%%%%
+    drawnow;
+
+    % Do EQ and find alpha's
+    len_tr      = 4096*2;
+    
+    ffe_order   = [50, 5, 5];
+    dfe_order   = [0, 0, 0];
+    pf_ncoeffs  = 1;
+    mu_ffe      = [0.0001, 0.0008, 0.001];
+    mu_dfe      = 0.0004;
+    mu_dc       = 0.005;
+    
+    %%% FULL RESP TARGET
+    eq_    = EQ("Ne",ffe_order,"Nb",dfe_order,"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",0);
+    pf_1    = Postfilter("ncoeff",1,"useBurg",1);
+    
+    [eq_signal_sd, eq_noise] = eq_.process(Scpe_sig, Symbols);
+
+    % eq_noise.signal = eq_noise.signal - mean(eq_noise.signal);
+    % eq_noise = eq_noise.normalize("mode","rms");
+    
+    [mlse_sig_sd,whitened_noise] = pf_1.process(eq_signal_sd, eq_noise);
+
+    fig = figure(fignum+dbmode+10); hold on
+    
+    [h, w] = freqz(1, pf_1.coefficients, length(eq_noise), "whole", eq_noise.fs);
+    h = h / max(abs(h)); % Normalize the filter response
+    w_ = (w - eq_noise.fs / 2);
+   
+    %%% DB TARGET
+    db_ref_sequence = Duobinary().encode(Symbols);
+     eq_    = EQ("Ne",ffe_order,"Nb",dfe_order,"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",0);
+    [eq_signal, db_noise] = eq_.process(Scpe_sig,db_ref_sequence);
+    
+    % db_noise.signal = db_noise.signal - mean(db_noise.signal);
+    % db_noise = db_noise.normalize("mode","rms");
+    
+    %%% 1-3) Plot EQ Noise EEN
+    figure(fignum+dbmode+10)
+    eq_noise.spectrum("displayname", 'Noise', "fignum", fignum+dbmode+10, "normalizeTo0dB", 0,"color",clr.Set1.green,"normalizeToDC",0,"addDCoffset",0);
+    if dbmode == 1
+        offset = 27.7;
+    else
+        offset = 29.8;
+    end
+    plot(w_ * 1e-9, 20 * log10(fftshift(abs(h)))-offset, 'DisplayName', ['Burg Coeffs: ', num2str(round(pf_1.coefficients, 2)), ' '], 'LineWidth', 1,'Color',clr.Set1.green,'LineStyle','--');
+    db_noise.spectrum("displayname", 'DBt. Noise', "fignum", fignum+dbmode+10, "normalizeTo0dB", 0,"color",clr.Set1.blue,"normalizeToDC",0,"addDCoffset",0);
+
+    ylim([-54,-25]);
+    xlim([0,105]);
+    xticks(0:20:110);
+    yticks(-50:10:10);
+
+    beautifyBERplot("logscale",0,"setmarkers",0)
+    pos = [100.3333  991.6667  358.0000  192.6667];
+    set(fig, 'Position', pos);
+
+end
+
+
+% === FINAL FIGURE SIZE ===

projects/HighSpeedExperiment_2024/Auswertung_JLT/final/FIGURE_WAVELENGTH.m

@@ -0,0 +1,124 @@
+%% ============================================================
+%  LOAD DATA
+% ============================================================
+database_type = 'mysql';
+db = DBHandler("dataBase", "labor_highspeed", "type", database_type);
+
+M = 4;  % PAM level for this analysis
+
+fp = QueryFilter();
+fp.where('Runs', 'pam_level',      'EQUALS', M);
+fp.where('Runs', 'fiber_length',   'EQUALS', 10);
+fp.where('Runs', 'bitrate',        'EQUALS', 360e9);
+fp.where('Runs', 'is_mpi',         'EQUALS', 0);
+
+fields = [
+    db.getTableFieldNames('power_state_info');
+    db.getTableFieldNames('dashboard_ungrouped_alltime')
+];
+
+[dataTable, ~] = db.queryDB(fp, fields);
+
+
+%% ============================================================
+%  COMMON CONFIGURATION FOR ALL SUBPLOTS
+% ============================================================
+%% ============================================================
+%  DEFINE DSP ALGORITHMS FOR THE 4 SUBPLOTS
+% ============================================================
+curves = struct;
+
+curves(1).name  = 'VNLE';
+curves(1).eq    = equalizer_structure.vnle;
+curves(1).pre   = 0;
+curves(1).color = clr.Paired.red;
+
+curves(2).name  = 'PF + MLSE';
+curves(2).eq    = equalizer_structure.vnle_pf_mlse;
+curves(2).pre   = 0;
+curves(2).color = clr.Paired.green;
+
+curves(3).name  = 'DB-target + MLSE';
+curves(3).eq    = equalizer_structure.vnle_db_mlse;
+curves(3).pre   = 0;
+curves(3).color = clr.Paired.blue;
+
+curves(4).name  = 'ML-based MLSE';
+curves(4).eq    = equalizer_structure.ml_mlse;
+if M == 4
+curves(4).pre   = 0;
+else
+curves(4).pre   = 1;
+end
+curves(4).color = clr.Paired.purple;
+
+
+%% ============================================================
+%  ANALYSIS ENGINE — NO PLOTTING
+% ============================================================
+results = struct;
+
+for k = 1:numel(curves)
+
+    %% ---- BASE CONFIG ----
+    cfg = struct;
+    cfg.x_axis   = 'wavelength';
+    cfg.y_axis   = 'BER';
+    cfg.agg      = 'min';
+    cfg.outlier  = 'none';
+    % cfg.group_by = {'wavelength'};
+    cfg.show_raw = false;
+
+    cfg.filters = struct( ...
+        'pam_level',    M, ...
+        'is_mpi',       0, ...
+        'bitrate',      360e9, ...
+        'fiber_length', 10, ...
+        'equalizer_structure', curves(k).eq, ...
+        'pre_emph',     curves(k).pre);
+
+    %% ---- GET BER ----
+    cfg.y_axis = 'BER';
+    A = analyze_measurements_gpt(dataTable, cfg);
+
+    results(k).wavelength = A.group{1}.x;
+
+    if curves(k).eq == equalizer_structure.vnle_db_mlse || ...
+       curves(k).eq == equalizer_structure.ml_mlse
+        % DB and ML-based need precoded BER
+        results(k).ber = A.group{1}.y_precoded;
+    else
+        results(k).ber = A.group{1}.y;
+    end
+
+end
+
+%% ============================================================
+%  1×4 TILED BER-vs-WAVELENGTH FIGURE
+% ============================================================
+fig=figure(901); 
+tiledlayout(1,4,'TileSpacing','compact','Padding','compact');
+
+lw = 1.8;    % line width
+ms = 6;      % marker size
+
+for k = 1:numel(curves)
+    nexttile; hold on;
+
+    plot(results(k).wavelength, results(k).ber, ...
+        '-o', ...
+        'Color', curves(k).color, ...
+        'MarkerFaceColor', curves(k).color, ...
+        'MarkerSize', ms, ...
+        'LineWidth', lw);
+
+    set(gca,'YScale','log');
+    grid on;
+    xlabel('wavelength');
+    ylabel('BER');
+    title(curves(k).name);
+    ylim([1e-4, 0.1])
+    beautifyBERplot();
+end
+pos = 1e3.*[0.1070    0.5497    1.4113    0.3253];
+set(fig, 'Position', pos);