Minimal changes
Add Theory plots for Silas Diss
This commit is contained in:
Silas Oettinghaus
@@ -1,3 +1,499 @@
|
||||
% classdef ML_MLSE < handle
|
||||
% % ALGORITHM DESCRIBED IN:
|
||||
% % W. Lanneer and Y. Lefevre, “Machine Learning-Based Pre-Equalizers for
|
||||
% % Maximum Likelihood Sequence Estimation in High-Speed PONs,”
|
||||
% % in 2023 31st European Signal Processing Conference
|
||||
%
|
||||
% % Further ML Refs:
|
||||
% % https://machinelearningmastery.com/cross-entropy-for-machine-learning/
|
||||
% % https://docs.pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html
|
||||
%
|
||||
% % The central idea is to overcome the (white-) noise assumption within the previously described
|
||||
% % Viterbi algorithm, more precisely a closed-loop optimization is proposed that finds a suitable
|
||||
% % filter-set to directly compute the branch metrics c_k (s,s^' ). These can directly be used to
|
||||
% % carry out the conventional Viterbi algorithm. The system consists of S^L S=F linear FIR filters,
|
||||
% % combined with one bias coefficient respectively. These filters take the received input samples to
|
||||
% % compute the branch metrics estimates (c_k ) ̂(s,s^' ) according toThe central idea is to overcome
|
||||
% % the (white-) noise assumption within the previously described Viterbi algorithm, more precisely
|
||||
% % a closed-loop optimization is proposed that finds a suitable filter-set to directly compute the
|
||||
% % branch metrics c_k (s,s^' ). These can directly be used to carry out the conventional Viterbi
|
||||
% % algorithm. The system consists of S^L S=F linear FIR filters, combined with one bias coefficient
|
||||
% % respectively. These filters take the received input samples to compute the branch metrics
|
||||
% % estimates. Finally, the usual Viterbi is carried out...
|
||||
%
|
||||
% % Recommended Settings and some findings:
|
||||
%
|
||||
% % Requires many training epochs. According to ML people, 100,200 or
|
||||
% % even up to 1000 epochs are normal for ML-convergence
|
||||
%
|
||||
% % The mu parameter _can_ be adaptive - using the cross entropy and when
|
||||
% % analyzing the isolated training it looks very promisig. However, is
|
||||
% % later use I found this is not as stable as a fixed learning rate.
|
||||
% % mu = 0.1 worked good for me
|
||||
%
|
||||
% % Longer orders/ filter length are not always better. For me order=11
|
||||
% % was good.
|
||||
%
|
||||
% % Delay factor (delta) is good when the order is also increased. With
|
||||
% % order = 11, a delta of =4 shows good results
|
||||
%
|
||||
% 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
|
||||
%
|
||||
% adaptive_mu
|
||||
%
|
||||
% constellation
|
||||
%
|
||||
% L %viterbi memory length
|
||||
%
|
||||
% alpha
|
||||
% DIR
|
||||
% DIR_flip
|
||||
% trellis_states
|
||||
%
|
||||
% traceback_depth
|
||||
%
|
||||
% % --- Added internal class variables used later ---
|
||||
% S
|
||||
% Nf
|
||||
% delta
|
||||
% nStates
|
||||
% nFeasible
|
||||
% combs
|
||||
% first_sym
|
||||
% last_sym
|
||||
% valid
|
||||
% valid_to_idx
|
||||
% valid_from_idx
|
||||
% w
|
||||
%
|
||||
% % --- New: fast state lookup ---
|
||||
% true_to_state_idx
|
||||
% state_dict % containers.Map: key(sequence)->state index
|
||||
% key_fmt = '%.8g_'; % key format for sequence strings
|
||||
% nSym % |constellation|
|
||||
%
|
||||
% 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;
|
||||
% 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
|
||||
%
|
||||
% function [X,X_viterbi] = process(obj, X, D)
|
||||
%
|
||||
% % actual processing of the signal (steps 1. - 3.)
|
||||
% % 1 normalize RMS
|
||||
% X = X.normalize("mode","rms");
|
||||
%
|
||||
% % Use sorted constellation for deterministic mapping
|
||||
% 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
|
||||
%
|
||||
% % ==============================================================
|
||||
% % INITIALIZATION (only before final epoch and detection mode)
|
||||
% % ==============================================================
|
||||
%
|
||||
% % --- Parameters
|
||||
% obj.S = numel(obj.constellation); % alphabet size
|
||||
% obj.Nf = obj.order*obj.sps; % filter length
|
||||
% % obj.delta = 3;%ceil(obj.Nf/2); % delay parameter
|
||||
% 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{:}).'); % rows: states, columns: [x_k, x_{k-1}, ...]
|
||||
% 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);
|
||||
%
|
||||
% % --- Allocate vectors and weights
|
||||
% % !! IF SHAPE FIT, then we already have smth there an we want
|
||||
% % to start with the existing fitler-set
|
||||
% if isempty(obj.w) || any(size(obj.w) ~= [obj.Nf+1,obj.nFeasible])
|
||||
% obj.w = zeros(obj.Nf+1,obj.nFeasible); % filter weights per transition + bias tap
|
||||
% obj.w = randn(obj.Nf+1,obj.nFeasible);
|
||||
% end
|
||||
%
|
||||
% % --- Precompute dictionary for fast state lookup (sequence -> state)
|
||||
% keys = cell(obj.nStates,1);
|
||||
% for i = 1:obj.nStates
|
||||
% keys{i} = obj.seq_key(obj.combs(i,:)); % combs row is already [x_k, x_{k-1}, ...]
|
||||
% end
|
||||
% obj.state_dict = containers.Map(keys, 1:obj.nStates);
|
||||
%
|
||||
% % ==============================================================
|
||||
% % TRAINING
|
||||
% % ==============================================================
|
||||
%
|
||||
% % Training Mode
|
||||
% n = obj.len_tr;
|
||||
% training = 1;
|
||||
% obj.equalize(X.signal, D.signal,obj.mu_tr,obj.epochs_tr,n,training);
|
||||
% obj.e_tr = obj.e;
|
||||
%
|
||||
% % ==============================================================
|
||||
% % DD-Mode / Fixed Mode
|
||||
% % ==============================================================
|
||||
%
|
||||
% % 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_ref] = equalize(obj,x,d,mu,epochs,N,training)
|
||||
% % ==============================================================
|
||||
% % FFE + Whitening + ML-Based Branch Metric Estimation + Viterbi
|
||||
% % ==============================================================
|
||||
% debug = 1;
|
||||
% showPlots = 1;
|
||||
%
|
||||
% % --- Input padding and preallocation
|
||||
% y = zeros(N,1);
|
||||
%
|
||||
% % number of symbol steps in this block
|
||||
% nSymbols = ceil(N/obj.sps);
|
||||
%
|
||||
% for epoch = 1:epochs
|
||||
%
|
||||
% % state metrics (log-domain costs): keep as column [nStates×1]
|
||||
% pm = zeros(obj.nStates,1); % v_{k-1}(s′)
|
||||
% c_hat = zeros(1,obj.nFeasible);
|
||||
% v_tilde = zeros(1,obj.nFeasible);
|
||||
% pred = zeros(nSymbols, obj.nStates, 'uint32');
|
||||
% pm_sto = nan(obj.nStates, nSymbols,'like',pm);
|
||||
% CE_accum = 0;
|
||||
%
|
||||
%
|
||||
% %%% START IDX
|
||||
% if training
|
||||
% max_start = length(x) - ( (ceil(N/obj.sps)-1)*obj.sps + 1 );
|
||||
% max_start = max(1, max_start); % safety
|
||||
% start_sample = randi([1, max_start], 1); %rnd training; not really good
|
||||
% start_sample = 1;
|
||||
% end_sample = start_sample + (ceil(N/obj.sps)-1)*obj.sps;
|
||||
% else
|
||||
% start_sample = 1;%obj.len_tr;
|
||||
% end_sample = N;
|
||||
% end
|
||||
%
|
||||
% start_symbol = 1 + floor((start_sample - 1)/obj.sps); % ABSOLUTE symbol index
|
||||
%
|
||||
% if numel(d) >= obj.L && start_symbol >= obj.L
|
||||
% init_seq = d(start_symbol-obj.L+1 : start_symbol); % [d_k-L+1 ... d_k]
|
||||
% true_to_state_idx = obj.state_dict(obj.seq_key(flip(init_seq))); % [d_k ... d_k-L+1]
|
||||
% else
|
||||
% % Not enough history – fall back to state 1
|
||||
% true_to_state_idx = uint32(1);
|
||||
% end
|
||||
%
|
||||
% symbol = 0;
|
||||
% for sample = start_sample:obj.sps:end_sample
|
||||
% symbol = symbol + 1;
|
||||
% k = symbol;
|
||||
% sym_idx = start_symbol + (symbol - 1);
|
||||
%
|
||||
% % --- Build Δ-delayed observation window y_k
|
||||
% 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)]; % Nf×1
|
||||
% yk = [yk;1];
|
||||
%
|
||||
% % --- Predict branch metrics for all feasible transitions: c_hat
|
||||
% c_hat = (yk.' * obj.w); % [1×nFeasible]
|
||||
% c_hat = c_hat.'; % [nFeasible×1]
|
||||
%
|
||||
% % --- Extended path metrics: v_tilde = pm(from) + c_hat
|
||||
% % normalize pm to avoid growth (invariant to additive const)
|
||||
% pm = pm - min(pm);
|
||||
% v_tilde = pm(obj.valid_from_idx) + c_hat; % [nFeasible×1]
|
||||
%
|
||||
% % ===== Gradient update (Algorithm 1) =====
|
||||
%
|
||||
% if 1 %training
|
||||
% % --- allocate storage once
|
||||
% if epoch == 1 && symbol == 1
|
||||
% obj.true_to_state_idx = ones(ceil(N/obj.sps),1,'uint32');
|
||||
% end
|
||||
%
|
||||
% % --- previous "to" becomes current "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
|
||||
% % only compute in first epoch
|
||||
% if sym_idx >= obj.L
|
||||
% key_to = obj.seq_key(flip(d(sym_idx-obj.L+1 : sym_idx)));
|
||||
% if isKey(obj.state_dict, key_to)
|
||||
% obj.true_to_state_idx(symbol) = obj.state_dict(key_to);
|
||||
% else
|
||||
% obj.true_to_state_idx(symbol) = true_from_state_idx;
|
||||
% end
|
||||
% else
|
||||
% obj.true_to_state_idx(symbol) = true_from_state_idx;
|
||||
% end
|
||||
% end
|
||||
%
|
||||
% % --- reuse cached state from second epoch onward
|
||||
% true_to_state_idx = obj.true_to_state_idx(symbol);
|
||||
%
|
||||
% % --- ensure valid (from,to)
|
||||
% dirac = zeros(obj.nFeasible,1);
|
||||
% mask = obj.valid_from_idx==true_from_state_idx & ...
|
||||
% obj.valid_to_idx ==true_to_state_idx;
|
||||
% if any(mask)
|
||||
% dirac(mask) = 1;
|
||||
% else
|
||||
% idx = find(obj.valid_from_idx==true_from_state_idx,1,'first');
|
||||
% dirac(idx) = 1;
|
||||
% obj.true_to_state_idx(symbol) = obj.valid_to_idx(idx);
|
||||
% end
|
||||
%
|
||||
%
|
||||
%
|
||||
%
|
||||
% % softmax over -v_tilde (numerically safe shift)
|
||||
% v_shift = -(v_tilde - min(v_tilde)); % shift to small positive numbers
|
||||
% v_shift = min(v_shift, 100); % clamp exponent argument (≈ exp(50)=3e21)
|
||||
% expv = exp(v_shift);
|
||||
% p = expv ./ (sum(expv) + eps);
|
||||
%
|
||||
% % for logging only:
|
||||
% CE_symbol(symbol) = -log(p(dirac==1) + eps);
|
||||
%
|
||||
% if sym_idx > obj.L
|
||||
% CE_smooth(symbol) = 0.01*CE_symbol(symbol) + 0.99*CE_smooth(symbol-1);
|
||||
% else
|
||||
% if epoch > 1
|
||||
% CE_smooth(symbol) = obj.ce(end); %use ce from last epoch or =1 for very first round?!
|
||||
% else
|
||||
% CE_smooth(symbol) = CE_symbol(symbol);
|
||||
% end
|
||||
% end
|
||||
%
|
||||
% CE_accum = CE_symbol(symbol) + CE_accum;
|
||||
%
|
||||
%
|
||||
% % gradient term (t - p)
|
||||
% dmp = (dirac - p)'; % 1×nFeasible
|
||||
%
|
||||
% % Per-feature gradient; implicit expansion gives (Nf+1)×nFeasible
|
||||
% dL_Dw = (yk) .* dmp;
|
||||
%
|
||||
% % Start updates only when the ABSOLUTE symbol index has ≥ L history
|
||||
% if sym_idx >= obj.L
|
||||
% if obj.adaptive_mu
|
||||
% mu_eff = CE_smooth(sym_idx);
|
||||
% mu_eff = max(min(mu_eff, 0.2), 1e-4);
|
||||
% else
|
||||
% mu_eff = mu;
|
||||
% end
|
||||
%
|
||||
% obj.w = obj.w - mu_eff .* dL_Dw; % (Nf+1)×nFeasible
|
||||
% end
|
||||
%
|
||||
% % if debug && epoch > 2
|
||||
% % figure(100);
|
||||
% % subplot(4,1,1);
|
||||
% % heatmap(p');
|
||||
% % title('Probs')
|
||||
% % subplot(4,1,2);
|
||||
% % heatmap(dmp);
|
||||
% % title('Update')
|
||||
% % subplot(4,1,3);
|
||||
% % heatmap(dL_Dw);
|
||||
% % title('Update')
|
||||
% % subplot(4,1,4);
|
||||
% % heatmap(bj.w);
|
||||
% % title('Update')
|
||||
% %
|
||||
% % end
|
||||
%
|
||||
% end
|
||||
%
|
||||
%
|
||||
%
|
||||
% % --- Compare-Select (matrix form, min of costs)
|
||||
% v_tilde_mat = inf(obj.nStates, obj.nStates);
|
||||
% v_tilde_mat(obj.valid) = v_tilde;
|
||||
% [pm_next, pred(k,:)] = min(v_tilde_mat, [], 2);
|
||||
%
|
||||
% % re-center to keep metrics bounded (decision-invariant)
|
||||
% pm_next = pm_next - min(pm_next);
|
||||
%
|
||||
% pm = pm_next;
|
||||
% pm_sto(:,symbol) = pm;
|
||||
% end
|
||||
%
|
||||
% % --- Traceback (full; you can window with traceback_depth if desired)
|
||||
% [~, s_end] = min(pm);
|
||||
% viterbi_path = zeros(symbol,1,'uint32');
|
||||
% viterbi_path(symbol) = s_end;
|
||||
% for n = symbol:-1:2
|
||||
% viterbi_path(n-1) = pred(n, viterbi_path(n));
|
||||
% end
|
||||
%
|
||||
% y_ref = d(start_symbol:end);
|
||||
% y = obj.first_sym(viterbi_path);
|
||||
%
|
||||
% if debug && training
|
||||
% sym_start = start_symbol;
|
||||
% sym_end = start_symbol + symbol - 1;
|
||||
% ref_slice = d(sym_start : sym_end);
|
||||
% err = sum(y ~= ref_slice(1:numel(y)));
|
||||
%
|
||||
% try
|
||||
% ref_bits = PAMmapper(obj.S,0).demap(ref_slice);
|
||||
% 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: %.1e \n',epoch, ber);
|
||||
% obj.ber(epoch) = ber;
|
||||
% catch
|
||||
% ser = err./length(y);
|
||||
% fprintf('Epoch: %d - SER: %.1e \n',epoch, ser);
|
||||
% end
|
||||
%
|
||||
% obj.ce(epoch) = CE_accum./symbol;
|
||||
%
|
||||
% if showPlots
|
||||
% figure(10);clf
|
||||
% subplot(3,2,1:2);
|
||||
% heatmap(obj.w);
|
||||
% title('Filter')
|
||||
%
|
||||
% subplot(3,2,3);
|
||||
% v_tildemat = NaN(obj.nStates, obj.nStates);
|
||||
% v_tildemat(obj.valid) = v_tilde; % log-domain scores
|
||||
% heatmap(v_tildemat);
|
||||
% title('Path Metrics (v_tilde)')
|
||||
%
|
||||
% subplot(3,2,4);
|
||||
% scatter(1:symbol,pm_sto,1,'.')
|
||||
% title('Path Metric Winners')
|
||||
%
|
||||
% 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
|
||||
%
|
||||
% % Left y-axis: Cross Entropy (linear)
|
||||
% yyaxis left
|
||||
% scatter(1:length(obj.ce), obj.ce, 10, 's', 'filled')
|
||||
% ylabel('Cross Entropy')
|
||||
%
|
||||
% % Right y-axis: BER (logarithmic)
|
||||
% yyaxis right
|
||||
% scatter(1:length(obj.ber), obj.ber, 10, 'd', 'filled')
|
||||
% set(gca, 'YScale', 'log')
|
||||
% ylabel('BER (log scale)')
|
||||
%
|
||||
% xlim([1, epochs])
|
||||
% xlabel('Epoch')
|
||||
% title('Cross Entropy // BER')
|
||||
% grid on
|
||||
%
|
||||
% drawnow
|
||||
% end
|
||||
% end
|
||||
% end
|
||||
% end
|
||||
% end
|
||||
%
|
||||
% methods (Access=private)
|
||||
% function k = seq_key(obj, seq)
|
||||
% % Build a stable key string for a sequence row vector in the *same order as combs rows* ([x_k, x_{k-1}, ...])
|
||||
% % Use rounding via sprintf to avoid floating-point issues.
|
||||
% % seq must be a row vector.
|
||||
% k = sprintf(obj.key_fmt, seq);
|
||||
% end
|
||||
% end
|
||||
% end
|
||||
|
||||
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
|
||||
classdef ML_MLSE < handle
|
||||
% ---------------------------------------------------------------------
|
||||
% W. Lanneer and Y. Lefevre,
|
||||
@@ -101,7 +597,7 @@ classdef ML_MLSE < handle
|
||||
obj.S = obj.nSym;
|
||||
obj.Nf = obj.order * obj.sps;
|
||||
obj.nStates = obj.S^obj.L;
|
||||
obj.nFeasible = obj.nStates * obj.S;
|
||||
obj.nFeasible = obj.nStates * obj.S; %feasible state transitions
|
||||
|
||||
% --- Trellis mapping
|
||||
obj.trellis_states = reshape(obj.constellation,1,[]);
|
||||
@@ -164,8 +660,8 @@ classdef ML_MLSE < handle
|
||||
% EQUALIZE
|
||||
% ==============================================================
|
||||
function [y,y_ref] = equalize(obj,x,d,mu,epochs,N,training)
|
||||
debug = 0;
|
||||
showPlots = 0;
|
||||
debug = 1;
|
||||
showPlots = 1;
|
||||
y = zeros(N,1);
|
||||
nSymbols = ceil(N/obj.sps);
|
||||
|
||||
@@ -241,6 +737,19 @@ classdef ML_MLSE < handle
|
||||
dirac(trans_idx)=1;
|
||||
end
|
||||
|
||||
% --- ensure valid (from,to)
|
||||
if ~any(dirac)
|
||||
mask = obj.valid_from_idx==true_from_state_idx & ...
|
||||
obj.valid_to_idx ==true_to_state_idx;
|
||||
if any(mask)
|
||||
dirac(mask) = 1;
|
||||
else
|
||||
idx = find(obj.valid_from_idx==true_from_state_idx,1,'first');
|
||||
dirac(idx) = 1;
|
||||
obj.true_to_state_idx(symbol) = obj.valid_to_idx(idx);
|
||||
end
|
||||
end
|
||||
|
||||
% ===================================================================
|
||||
% TRAINING MODE (weight update)
|
||||
% ===================================================================
|
||||
|
||||
140
Functions/Theory/Dissertation/mach_zehnder_modulator.m
Normal file
140
Functions/Theory/Dissertation/mach_zehnder_modulator.m
Normal file
@@ -0,0 +1,140 @@
|
||||
% Minimal MZM transfer-function demo (sinusoidal drive) — aligned with your notation
|
||||
%
|
||||
% Implements exactly:
|
||||
% E_out(t) = E0 * exp(j*w0*t) * exp(-j*w0*L*n_eff/c0) * 1/2 * [ exp(-j*phi1(t)) + rho*exp(-j*phi2(t)) ]
|
||||
% with phi_{1,2}(t) = pi * v_{1,2}(t)/Vpi
|
||||
%
|
||||
% Push-pull:
|
||||
% v1(t) = +v_drive(t)/2 , v2(t) = -v_drive(t)/2 => phi1 = +pi/2 * v_drive/Vpi, phi2 = -pi/2 * v_drive/Vpi
|
||||
%
|
||||
% And the ideal TF (rho=1):
|
||||
% E_out/E_in = exp(-j*w0*L*n_eff/c0) * cos( (pi/2) * v_drive/Vpi )
|
||||
%
|
||||
% Note: E_in(t) = E0 * exp(j*w0*t) in this script.
|
||||
|
||||
% Parameters
|
||||
c0 = physconst('lightspeed'); % [m/s]
|
||||
lambda0 = 1310e-9; % [m]
|
||||
omega0 = 2*pi*c0/lambda0;
|
||||
|
||||
L = 5e-3; % [m] effective phase section length (set as needed)
|
||||
n_eff = 2.2; % [-] effective index (set as needed)
|
||||
|
||||
E0 = 1; % field amplitude (arbitrary)
|
||||
Vpi = 3.2; % [V] half-wave voltage (your V_pi)
|
||||
|
||||
% Drive
|
||||
f0 = 1e9; % [Hz]
|
||||
fs = 100e9; % [Hz]
|
||||
Nper = 1; % number of periods
|
||||
Vpp = 0.5*Vpi; % [V] peak-to-peak of v_drive(t)
|
||||
|
||||
biasV = 2; % [V] differential bias added to v_drive
|
||||
|
||||
% Analytic
|
||||
v_ = linspace(-1,2, 2001);
|
||||
% Field transfer function (amplitude)
|
||||
Field_mzm_analytic = cos((pi/2)*v_);
|
||||
% Power transfer function (intensity)
|
||||
P_mzm_analytic = Field_mzm_analytic.^2;
|
||||
|
||||
% Imbalance factor in YOUR notation:
|
||||
rho = 1; % rho=1 -> ideal balanced MZM (collapses to ideal TF)
|
||||
|
||||
% Time axis + differential drive voltage v_drive(t)
|
||||
T = Nper/f0;
|
||||
t = (0:1/fs:T-1/fs).';
|
||||
|
||||
v_drive = biasV + (Vpp/2)*sin(2*pi*f0*t); % v_drive(t) (peak = Vpp/2)
|
||||
|
||||
% Push-pull branch voltages (consistent with v_drive = v1 - v2)
|
||||
v1 = +0.5*v_drive; % arm 1
|
||||
v2 = -0.5*v_drive; % arm 2
|
||||
|
||||
% Phases phi1, phi2
|
||||
phi1 = pi * v1 / Vpi;
|
||||
phi2 = pi * v2 / Vpi;
|
||||
|
||||
% Fields: E_in and E_out (exactly your Eq. (mzm_e_field))
|
||||
E_in = E0 .* exp(1i*omega0*t);
|
||||
|
||||
common_phase = exp(-1i * (omega0*L*n_eff/c0)); % exp(-j*omega0*L*n_eff/c0)
|
||||
|
||||
E_out = E0 .* exp(1i*omega0*t) .* common_phase .* 0.5 .* ...
|
||||
( exp(-1i*phi1) + rho .* exp(-1i*phi2) );
|
||||
|
||||
% Transfer function (numerical): E_out/E_in
|
||||
H_num = E_out ./ E_in;
|
||||
|
||||
% Power (normalized)
|
||||
Pnorm_num = abs(H_num).^2; % since |E_out/E_in|^2
|
||||
|
||||
|
||||
|
||||
% Ideal TF (analytic) for comparison (rho=1, push-pull)
|
||||
H_ideal = common_phase .* cos( (pi/2) * (v_drive./Vpi) );
|
||||
|
||||
Pnorm_ideal = abs(H_ideal).^2;
|
||||
Pnorm_math = cos( (pi/2) * (v_drive./Vpi) ).^2;
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
set(groot, 'defaultLegendInterpreter', 'tex');
|
||||
set(groot, 'defaultAxesTickLabelInterpreter', 'tex');
|
||||
set(groot, 'defaultTextInterpreter', 'tex');
|
||||
|
||||
% Normalized voltage axis (multiples of Vpi)
|
||||
v_norm = v_drive./Vpi;
|
||||
|
||||
colfield = [0,0,0]; %is black
|
||||
colpow = linspecer(2);
|
||||
colpow = colpow(1,:);
|
||||
colvdrive = linspecer(2);
|
||||
colvdrive = colvdrive(2,:);
|
||||
|
||||
%% SIGNAL IN
|
||||
figure(1); clf
|
||||
plot(v_norm,t*1e9, 'LineWidth', 1.0,'Color',colvdrive); grid on;
|
||||
ylabel('t [ns]'); xlabel('v_{drive}(t)/V_\pi');
|
||||
title('Drive voltage (normalized)');
|
||||
xlim([min(v_) max(v_)]);
|
||||
|
||||
%% IN/OUT (static transfer) — normalized x-axis + analytic curve
|
||||
figure(2); clf
|
||||
plot(v_, Field_mzm_analytic, 'LineWidth', 1.2,'LineStyle','--','Color',colfield); hold on;% analytic power TF
|
||||
plot(v_, P_mzm_analytic, 'LineWidth', 1.2, 'Color',colpow); hold on;% analytic power TF
|
||||
% show input time signal
|
||||
plot(v_norm,-1+t*1e9, 'LineWidth', 1.0,'Color',colvdrive); grid on;
|
||||
% show output time signal
|
||||
plot(2+t*1e9, Pnorm_num, 'LineWidth', 1.0,'DisplayName','Intensity', 'Color',colvdrive); hold on;
|
||||
plot(2+t*1e9, real(H_ideal), '--', 'LineWidth', 1.0,'DisplayName','Field','Color',colfield); hold on;
|
||||
scatter(v_norm, Pnorm_num, 12, '.', 'LineWidth', 1,'MarkerEdgeColor',colvdrive);
|
||||
scatter(biasV./Vpi,(cos((pi/2)*biasV./Vpi)^2),10,'Marker','o');
|
||||
line([min(v_drive), min(v_drive)]./Vpi,[(cos((pi/2)*min(v_drive)./Vpi)^2), -2],'linewidth',0.5,'color','black','linestyle','--');
|
||||
line([max(v_drive) max(v_drive)]./Vpi,[(cos((pi/2)*max(v_drive)./Vpi)^2), -2],'linewidth',0.5,'color','black','linestyle','--');
|
||||
xline([min(v_norm) max(v_norm)])
|
||||
|
||||
grid on;
|
||||
xlabel('v_{drive}(t)/V_\pi'); ylabel('|E_{out}/E_{in}|^2');
|
||||
% legend
|
||||
xlim([min(v_) max(v_)+1]);
|
||||
ylim([-1 1]);
|
||||
|
||||
% mat2tikz_improved('C:\Users\Silas\Documents\6971e0b65b380ca6d71c837f\02_IMDD_System\tikz\mzm.tex');
|
||||
|
||||
|
||||
%%
|
||||
% % FIELD TF (only field here; do not mix power into this figure)
|
||||
figure(3); clf
|
||||
% plot(t*1e9, real(H_num), 'LineWidth', 1.0); hold on;
|
||||
% plot(t*1e9, real(H_ideal), '--', 'LineWidth', 1.0,'DisplayName','Field','Color',colfield); hold on;
|
||||
plot(t*1e9, Pnorm_num, 'LineWidth', 1.0,'DisplayName','Intensity', 'Color',colpow); hold on;
|
||||
grid on;
|
||||
xlabel('t [ns]'); ylabel('Re\{E_{out}/E_{in}\}');
|
||||
legend
|
||||
mat2tikz_improved('C:\Users\Silas\Documents\6971e0b65b380ca6d71c837f\02_IMDD_System\tikz\mzm_out.tex');
|
||||
|
||||
|
||||
|
||||
23
Functions/mat2tikz_improved.m
Normal file
23
Functions/mat2tikz_improved.m
Normal file
@@ -0,0 +1,23 @@
|
||||
function mat2tikz_improved(filename)
|
||||
arguments
|
||||
% Default to the path in your example if no argument is provided
|
||||
filename (1,1) string = 'C:\Users\Silas\Documents\Dissertation\00_Examples\tikz\textfig.tikz';
|
||||
end
|
||||
cleanfigure;
|
||||
matlab2tikz(char(filename), ...
|
||||
'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=1',...
|
||||
'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}',...
|
||||
'legend style={at={(0.02,0.98)},draw=white!0!white,font=\scriptsize,inner sep=0.1pt,fill=white,column sep=1pt,anchor= north west}',...
|
||||
'every axis/.append style={font=\scriptsize}',...
|
||||
});
|
||||
|
||||
end
|
||||
@@ -1,10 +1,13 @@
|
||||
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
|
||||
% cmap = WesPalette.Zissou1.rgb() % full palette
|
||||
% cmap = WesPalette.Zissou1.rgb(3) % 3 colors (discrete default)
|
||||
% cmap = WesPalette.Zissou1.rgb(12,"discrete") % any n, no interpolation
|
||||
% cmap = WesPalette.Zissou1.rgb(256,"continuous") % smooth colormap (Lab interpolation)
|
||||
%
|
||||
% Requires:
|
||||
% - colorspace.m (Pascal Getreuer) on MATLAB path for "continuous" mode
|
||||
|
||||
enumeration
|
||||
BottleRocket1
|
||||
@@ -34,21 +37,33 @@ classdef WesPalette
|
||||
end
|
||||
|
||||
methods
|
||||
function cmap = rgb(obj, n)
|
||||
function cmap = rgb(obj, n, mode)
|
||||
% Return palette as Nx3 RGB colormap [0–1]
|
||||
%
|
||||
% n : number of requested colors (optional)
|
||||
% mode : "discrete" (default) or "continuous"
|
||||
|
||||
hex = obj.hex();
|
||||
base_hex = obj.hex();
|
||||
base_rgb = WesPalette.hex2rgb(base_hex);
|
||||
|
||||
rgb = hex2rgb(hex);
|
||||
|
||||
if nargin == 2
|
||||
if n > size(rgb,1)
|
||||
error('Requested %d colors, but only %d available.', ...
|
||||
n, size(rgb,1))
|
||||
if nargin < 2 || isempty(n)
|
||||
cmap = base_rgb;
|
||||
return;
|
||||
end
|
||||
cmap = rgb(1:n,:);
|
||||
if nargin < 3 || isempty(mode)
|
||||
mode = "discrete";
|
||||
end
|
||||
mode = lower(string(mode));
|
||||
|
||||
validateattributes(n, {'numeric'}, {'scalar','integer','positive'}, mfilename, 'n');
|
||||
if mode ~= "discrete" && mode ~= "continuous"
|
||||
error('mode must be "discrete" or "continuous".');
|
||||
end
|
||||
|
||||
if mode == "discrete"
|
||||
cmap = WesPalette.sample_discrete(base_rgb, n);
|
||||
else
|
||||
cmap = rgb;
|
||||
cmap = WesPalette.interpolate_continuous_lab(base_rgb, n);
|
||||
end
|
||||
end
|
||||
end
|
||||
@@ -56,78 +71,116 @@ classdef WesPalette
|
||||
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
|
||||
|
||||
methods (Static, Access = private)
|
||||
function rgb = hex2rgb(hex)
|
||||
% hex: cellstr like {'#RRGGBB', ...}
|
||||
if isstring(hex), hex = cellstr(hex); end
|
||||
n = numel(hex);
|
||||
rgb = zeros(n,3);
|
||||
for i = 1:n
|
||||
h = char(hex{i});
|
||||
if startsWith(h,'#'), h = h(2:end); end
|
||||
if numel(h) ~= 6
|
||||
error('Invalid HEX color: %s', hex{i});
|
||||
end
|
||||
rgb(i,1) = hex2dec(h(1:2))/255;
|
||||
rgb(i,2) = hex2dec(h(3:4))/255;
|
||||
rgb(i,3) = hex2dec(h(5:6))/255;
|
||||
end
|
||||
end
|
||||
|
||||
function cmap = sample_discrete(base_rgb, n)
|
||||
% No interpolation; allow any n by sampling/repeating.
|
||||
k = size(base_rgb,1);
|
||||
|
||||
if n <= k
|
||||
idx = round(linspace(1, k, n)); % spread across palette
|
||||
idx = max(1, min(k, idx));
|
||||
cmap = base_rgb(idx,:);
|
||||
else
|
||||
reps = floor(n / k);
|
||||
rmd = mod(n, k);
|
||||
cmap = [repmat(base_rgb, reps, 1); base_rgb(1:rmd,:)];
|
||||
end
|
||||
end
|
||||
|
||||
function cmap = interpolate_continuous_lab(base_rgb, n)
|
||||
% Smooth interpolation in Lab using colorspace().
|
||||
% Requires colorspace.m by Pascal Getreuer on MATLAB path.
|
||||
|
||||
k = size(base_rgb,1);
|
||||
if k == 1
|
||||
cmap = repmat(base_rgb, n, 1);
|
||||
return;
|
||||
end
|
||||
|
||||
% Convert to Lab, interpolate each channel, convert back
|
||||
lab = colorspace('Lab<-RGB', base_rgb);
|
||||
|
||||
t_base = linspace(0, 1, k);
|
||||
t_new = linspace(0, 1, n);
|
||||
|
||||
lab_new = zeros(n,3);
|
||||
for c = 1:3
|
||||
lab_new(:,c) = interp1(t_base, lab(:,c), t_new, 'linear');
|
||||
end
|
||||
|
||||
rgb_new = colorspace('RGB<-Lab', lab_new);
|
||||
|
||||
% Clamp to displayable gamut
|
||||
cmap = min(max(rgb_new, 0), 1);
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
@@ -5,8 +5,8 @@ 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);
|
||||
cmap = WesPalette.AsteroidCity1;
|
||||
% 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);
|
||||
|
||||
@@ -6,7 +6,7 @@ db = DBHandler("dataBase", "labor_highspeed", "type", database_type);
|
||||
|
||||
pam_levels = [4, 6, 8]; % three tiles
|
||||
bitrate_set = 360e9;
|
||||
fiberL = 10;
|
||||
fiberL = 2;
|
||||
|
||||
fields = [
|
||||
db.getTableFieldNames('power_state_info');
|
||||
@@ -96,7 +96,7 @@ end
|
||||
%% ============================================================
|
||||
% PLOT — 1×3 (PAM-4, PAM-6, PAM-8)
|
||||
% ============================================================
|
||||
fig = figure(9110); clf;
|
||||
fig = figure(9112); clf;
|
||||
tiledlayout(1,3,'TileSpacing','compact','Padding','compact');
|
||||
|
||||
lw = 1.8;
|
||||
@@ -223,19 +223,19 @@ ylabel('');
|
||||
|
||||
end
|
||||
|
||||
pos = 1e3.*[2.7770 1.2017 1.4000 0.3200];
|
||||
set(fig, 'Position', pos);
|
||||
% 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' ...
|
||||
});
|
||||
% 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' ...
|
||||
% });
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -7,113 +7,133 @@ data = readtable(tablename,"Delimiter",';','DecimalSeparator',',');
|
||||
% PLOT
|
||||
% ============================================================
|
||||
%% 1. DATA EXTRACTION & SETUP
|
||||
% Extract columns from the table
|
||||
%% 1. DATA EXTRACTION & SETUP
|
||||
raw_M = data.M;
|
||||
raw_baud = data.BaudRate;
|
||||
raw_net = data.NetRate;
|
||||
raw_names = string(data.ZoteroCode);
|
||||
raw_band = string(data.Band); % Spalte 'Band' als String extrahieren
|
||||
raw_codes = string(data.ZoteroCode);
|
||||
raw_names = string(data.Name);
|
||||
raw_band = string(data.Band);
|
||||
|
||||
% Filter: Keep only PAM 2, 4, 6, 8 and rows where Rates are not NaN
|
||||
% Filter Valid Data
|
||||
target_M = [2, 4, 6, 8];
|
||||
validIdx = ismember(raw_M, target_M) & ~isnan(raw_baud) & ~isnan(raw_net);
|
||||
|
||||
% Apply filter to create the working variables
|
||||
Mvals = raw_M(validIdx);
|
||||
baud = raw_baud(validIdx);
|
||||
netrate = raw_net(validIdx);
|
||||
codes = raw_codes(validIdx);
|
||||
names = raw_names(validIdx);
|
||||
bands = raw_band(validIdx); % Gefilterte Bands
|
||||
bands = raw_band(validIdx);
|
||||
|
||||
% Setup Lists for Formatting
|
||||
pam_list = target_M;
|
||||
colors = flip(cbrewer2('SET1',4));
|
||||
|
||||
% Visual Settings
|
||||
colors = lines(4); % 4 Farben für PAM-2,4,6,8
|
||||
|
||||
% 2. PLOT
|
||||
%% 2. PLOT (For Visual Check only)
|
||||
figure; hold on;
|
||||
ms = 40; % Marker size (etwas größer für bessere Sichtbarkeit)
|
||||
lw = 1.5; % Line width für Fit
|
||||
ms = 20;
|
||||
lw = 0.5;
|
||||
|
||||
for k = 1:length(pam_list) % Loop durch PAM-Formate
|
||||
for k = 1:length(pam_list)
|
||||
M = pam_list(k);
|
||||
|
||||
% Logical mask for current PAM
|
||||
idxPam = (Mvals == M);
|
||||
|
||||
% Extract for this PAM
|
||||
x = baud(idxPam);
|
||||
y = netrate(idxPam);
|
||||
b = bands(idxPam);
|
||||
n = names(idxPam);
|
||||
b = bands(idxPam); % Bands für diese PAM-Gruppe
|
||||
|
||||
% Get color for this PAM format
|
||||
col = colors(k,:);
|
||||
|
||||
% ----- LEGEND FLAG -----
|
||||
firstLegend = true;
|
||||
|
||||
% ---- PLOT ALL POINTS (Marker basierend auf Band) ----
|
||||
for i = 1:sum(idxPam)
|
||||
|
||||
% === MARKER LOGIC ===
|
||||
currentBand = b(i);
|
||||
if strcmpi(currentBand, 'O')
|
||||
marker = 'o'; % Kreis für O-Band
|
||||
elseif strcmpi(currentBand, 'C')
|
||||
marker = 'd'; % Diamond für C-Band
|
||||
% Marker Logic
|
||||
ms = 20;
|
||||
if strcmpi(b(i), 'O')
|
||||
marker = 'o';
|
||||
elseif strcmpi(b(i), 'C')
|
||||
marker = 'd';
|
||||
else
|
||||
marker = 's'; % Fallback (Quadrat), falls andere Bands auftauchen
|
||||
marker = 's';
|
||||
end
|
||||
if strcmpi(n(i), 'THIS WORK')
|
||||
marker = 'pentagram';
|
||||
ms = 100;
|
||||
end
|
||||
|
||||
% Plotting
|
||||
% Plot Scatter
|
||||
if firstLegend
|
||||
h = scatter(x(i), y(i), ms, ...
|
||||
'Marker', marker, ...
|
||||
'MarkerEdgeColor', col, ...
|
||||
'MarkerFaceColor', col, ...
|
||||
scatter(x(i), y(i), ms, 'Marker', marker, ...
|
||||
'MarkerEdgeColor', col, 'MarkerFaceColor', col, ...
|
||||
'DisplayName', sprintf('PAM-%d', M));
|
||||
firstLegend = false;
|
||||
else
|
||||
h = scatter(x(i), y(i), ms, ...
|
||||
'Marker', marker, ...
|
||||
'MarkerEdgeColor', col, ...
|
||||
'MarkerFaceColor', col, ...
|
||||
scatter(x(i), y(i), ms, 'Marker', marker, ...
|
||||
'MarkerEdgeColor', col, 'MarkerFaceColor', col, ...
|
||||
'HandleVisibility','off');
|
||||
end
|
||||
|
||||
% ====== CUSTOM DATATIP CONTENT ======
|
||||
dt = h.DataTipTemplate;
|
||||
dt.DataTipRows(1).Label = 'Baud rate';
|
||||
dt.DataTipRows(2).Label = 'Net rate';
|
||||
dt.DataTipRows(end+1) = dataTipTextRow('Band', b(i)); % Zeigt Band an
|
||||
dt.DataTipRows(end+1) = dataTipTextRow('Paper', n(i));
|
||||
end
|
||||
|
||||
% ---- Fit (PAM-specific) ----
|
||||
% Fit lines
|
||||
if length(x) >= 3
|
||||
[p, S, mu] = polyfit(x, y, 2);
|
||||
xfit = linspace(min(x), max(x), 200);
|
||||
yfit = polyval(p, xfit, S, mu);
|
||||
|
||||
plot(xfit, yfit, ':', ...
|
||||
'LineWidth', lw, ...
|
||||
'Color', col, ...
|
||||
'HandleVisibility', 'off');
|
||||
plot(xfit, yfit, '-', 'LineWidth', lw, 'Color', col, 'HandleVisibility', 'off');
|
||||
end
|
||||
end
|
||||
|
||||
grid on; box on;
|
||||
xlabel('Baud rate [GBd]');
|
||||
ylabel('Net rate [Gb/s]');
|
||||
title('Transmission Records (Circle=O, Diamond=C)');
|
||||
legend('Location','northwest');
|
||||
set(gca,'FontSize',11);
|
||||
% title('Check Command Window for TikZ Code');
|
||||
% legend('Location','northwest');
|
||||
|
||||
%% 3. GENERATE TIKZ ANNOTATION CODE
|
||||
% This prints the manual \draw commands to the console
|
||||
|
||||
%% GENERATE TIKZ ANNOTATION CODE
|
||||
% This prints the manual \draw commands to the console
|
||||
|
||||
%% GENERATE TIKZ ANNOTATION CODE (Colored Borders + Tiny Font)
|
||||
%% GENERATE TIKZ ANNOTATION CODE (No Arrow, Close Text)
|
||||
fprintf('\n\n%% ===========================================================\n');
|
||||
fprintf('%% COPY THE FOLLOWING LINES INTO YOUR .TEX FILE \n');
|
||||
fprintf('%% (Paste them just before \\end{axis})\n');
|
||||
fprintf('%% ===========================================================\n\n');
|
||||
|
||||
for i = 1:length(baud)
|
||||
bx = baud(i);
|
||||
by = netrate(i);
|
||||
key = codes(i);
|
||||
M_val = Mvals(i);
|
||||
|
||||
% --- PLACEMENT LOGIC ---
|
||||
if M_val == 8
|
||||
% PAM-8: Place Top-Left
|
||||
% 'south east' anchor means the text's bottom-right corner touches the coordinate
|
||||
% shift moves it slightly up and left to clear the marker
|
||||
anchorStr = 'south east';
|
||||
shiftStr = 'shift={(-3pt, 3pt)}';
|
||||
else
|
||||
% Others: Place Bottom-Right
|
||||
% 'north west' anchor means the text's top-left corner touches the coordinate
|
||||
% shift moves it slightly down and right
|
||||
anchorStr = 'north west';
|
||||
shiftStr = 'shift={(3pt, -3pt)}';
|
||||
end
|
||||
|
||||
% --- PRINT COMMAND ---
|
||||
% Uses \node directly at the coordinate (axis cs:...)
|
||||
fprintf('\\node[anchor=%s, %s, font=\\tiny, fill=white, inner sep=1pt] at (axis cs:%.2f, %.2f) {\\cite{%s}};\n', ...
|
||||
anchorStr, shiftStr, bx, by, key);
|
||||
end
|
||||
fprintf('\n')
|
||||
|
||||
%% === EXPORT ===
|
||||
outfile = 'C:\Users\Silas\Documents\latex\JLT_400G copy\media\matlab2tikz\highspeedresults.tikz';
|
||||
outfile = 'C:\Users\Silas\Documents\latex\JLT_400G_submission\media\matlab2tikz\highspeedresults_test.tikz';
|
||||
|
||||
matlab2tikz(outfile, ...
|
||||
'width','\fwidth', ...
|
||||
'height','\fheight', ...
|
||||
|
||||
@@ -25,10 +25,10 @@ h = abs([0.3 0.9 0.3]); h = h/norm(h);
|
||||
|
||||
% h = [1 -1.67085330039878 1.17918163282514 -0.805210559745616 0.571564213123367 -0.296337147529674 0.00649773445209780 0.0854177610195952 -0.0576009020965258 0.0520994427061551 -0.0624586034913656 0.0553280962699552 -0.00705582559925755 -0.0336399056707792 0.0706903719452810 -0.0334124287931977 0.0131699455037966 0.0587431373842994 -0.0515902976066452 0.00647904355473619 0.0137506750904990 -0.0547974515885928 0.00994735499340592 -0.0135513582534086 -0.00463322575007739 0.0277311946101940];
|
||||
% h = h/norm(h);
|
||||
symbols_filt = Symbols.filter(1,h);
|
||||
symbols_filt = Symbols.filter(h,1);
|
||||
symbols_noi = symbols_filt;
|
||||
symbols_noi.signal = awgn(symbols_filt.signal,SNR_dB,'measured');
|
||||
symbols_noi.spectrum;
|
||||
symbols_noi.spectrum();
|
||||
|
||||
% --- Generate all parameter pairs
|
||||
[O,D] = ndgrid(order_range, delta_range);
|
||||
@@ -42,7 +42,7 @@ ce_vec = nan(size(pairs,1),1);
|
||||
ce_training = nan(size(pairs,1),training_len);
|
||||
|
||||
% --- Parallel loop over parameter pairs
|
||||
parfor k = 1:size(pairs,1)
|
||||
for k = 1:size(pairs,1)
|
||||
order_k = pairs(k,1);
|
||||
delta_k = pairs(k,2);
|
||||
|
||||
@@ -55,7 +55,7 @@ parfor k = 1:size(pairs,1)
|
||||
try
|
||||
ml = ML_MLSE("epochs_tr",training_len,"epochs_dd",1,"len_tr",2^15, ...
|
||||
"mu_dd",0.1,"mu_tr",0.1,"order",order_k,"sps",1, ...
|
||||
"traceback_depth",128,"L",2,"delta",delta_k,"adaptive_mu",0);
|
||||
"traceback_depth",128,"L",3,"delta",delta_k,"adaptive_mu",0);
|
||||
|
||||
[y_ml,y_ref] = ml.process(symbols_noi,Symbols);
|
||||
ref_bits = PAMmapper(M,0).demap(y_ref);
|
||||
|
||||
Reference in New Issue
Block a user