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Gif stuff

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nonlinear MLSE investigation
trying hard to implment ML-based pre Equalization to find the branch metrics
This commit is contained in:
Silas Oettinghaus
2025-10-24 16:58:06 +02:00
parent c33264a515
commit 7085ba0931
16 changed files with 1414 additions and 162 deletions
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52
Classes/00_signals/Signal.m
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@@ -344,7 +344,7 @@ classdef Signal
arguments arguments
obj obj
options.fignum options.fignum = 2025
options.displayname = ""; options.displayname = "";
options.color = []; options.color = [];
options.normalizeToNyquist = 0; options.normalizeToNyquist = 0;
@@ -449,21 +449,25 @@ classdef Signal
ylabel(ylab); 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 try
ylim([max(min(floor(min(p_dbm))-3, ax.YLim(1)),-40), min(max(ceil(max(p_dbm))+3, ax.YLim(2)),10)]); yticks(round(linspace(y_min, y_max, min(10, max(4, ceil(y_range/10))))));
catch
ylim([floor(min(p_dbm,[],'all'))-3, ceil(max(p_dbm,[],'all'))+3]);
end 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; grid on;
legend
end end
@@ -961,8 +965,8 @@ classdef Signal
maxA = max(sig(100:end-100))*1.3; maxA = max(sig(100:end-100))*1.3;
minA = min(sig(100:end-100))*1.3; minA = min(sig(100:end-100))*1.3;
% maxA = 0.0015; maxA = 0.0025;
% minA = 0; minA = 0;
difference= maxA-minA; difference= maxA-minA;
@@ -1012,7 +1016,7 @@ classdef Signal
% add information % add information
if 1 if 0
pwr_dbm = round(obj.power,3); pwr_dbm = round(obj.power,3);
pwr_lin = obj.power("unit",power_notation.W); pwr_lin = obj.power("unit",power_notation.W);
@@ -1121,20 +1125,20 @@ classdef Signal
end 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 grid off
end 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 end
% disp('h'); % disp('h');

2
Classes/04_DSP/Equalizer/FFE.m
View File

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

233
Classes/04_DSP/Equalizer/FFE_MLSE.m Normal file
View File

@@ -0,0 +1,233 @@
classdef ML_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 = 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.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

246
Classes/04_DSP/Equalizer/ML_MLSE.m Normal file
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@@ -0,0 +1,246 @@
classdef ML_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 = 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.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 + ML-Based Branch Metric Estimation + Viterbi
% ==============================================================
% --- 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)); % alphabet size
L = obj.L; % MLSE memory
Nf = L; % filter length
Delta = ceil(L/2); % delay parameter
nStates = S^L;
nFeasible = S^(L-1)*S;
% --- Trellis mapping
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);
% --- Valid transitions
valid = false(nStates);
for from = 1:nStates
for to = 1:nStates
if all(combs(to,2:end) == combs(from,1:end-1))
valid(to,from) = true;
end
end
end
[valid_to, valid_from] = find(valid);
% --- Noise estimation
y_ideal = conv(d(:), obj.DIR(:), "same");
sigma2 = mean(abs(y - y_ideal).^2);
inv2s2 = 1/(2*sigma2);
% --- Allocate vectors and weights
pm = zeros(nStates,1);
w = zeros(Nf,nFeasible); % filter weights per transition
b = zeros(1,nFeasible); % bias terms
v_hat = zeros(1,nFeasible);
v_tilde = zeros(1,nFeasible);
bm_vec = zeros(1,nFeasible);
zi = zeros(max(numel(obj.DIR)-1,0),1);
end
% ==============================================================
% RUNTIME LOOP
% ==============================================================
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 / FFE adaptation
if training
d_hat = d(symbol);
else
[~,idx] = min(abs(y(symbol) - obj.constellation));
d_hat = obj.constellation(idx);
end
err = d_hat - y(symbol);
obj.e = obj.e + mu * (err * U);
% --- Whitening + MLSE in last epoch
if epoch == epochs && ~training
[y_white(symbol), zi] = filter(obj.DIR,1,y(symbol), zi);
k = symbol;
% --- Build Δ-delayed observation window y_k
i1 = k - Nf + 1 + Delta;
i2 = k + Delta;
buf = y_white(max(1,i1):min(length(y_white),i2));
padL = max(0,1 - i1);
padR = max(0,i2 - length(y_white));
yk = [zeros(padL,1); buf(:); zeros(padR,1)]; % Nf×1
% --- Predict branch metrics for all feasible transitions
v_hat = (yk.' * w) + b; % [1×nFeasible]
v_hat = v_hat.'; % [nFeasible×1]
% --- Extended path metrics
v_tilde = pm(valid_from) + v_hat; % [nFeasible×1]
% --- Compute branch metrics (distance)
bm_vec = -(y_white(k) - v_hat).^2 * inv2s2; % 1×nFeasible
% --- Survivor selection (vector aggregation)
pm_new_vec = pm(valid_from) + bm_vec.'; % nFeasible×1
pm_next = -inf(nStates,1);
surv_idx = zeros(nStates,1);
for t = 1:nStates
mask = (valid_to==t);
[pm_next(t), arg] = max(pm_new_vec(mask));
surv_idx(t) = valid_from(find(mask,1,'first')-1+arg);
end
pm = pm_next;
% --- Traceback
if mod(symbol,obj.traceback_depth) == 0
[~,viterbi_path(symbol)] = max(pm);
for n = symbol:-1:symbol-obj.traceback_depth+2
viterbi_path(n-1) = surv_idx(viterbi_path(n));
end
end
end
end
% --- Output reconstructed path
if epoch == epochs && ~training
y_vit = first_sym(viterbi_path);
end
end
end
end
end

114
Classes/04_DSP/Sequence Detection/MLSE.m
View File

@@ -277,14 +277,107 @@ classdef MLSE < handle
if debug if debug
alpha_ = alpha - min(alpha) + eps; % alpha_ = alpha - min(alpha) + eps;
figure();hold on; % figure();hold on;
n = 10; % 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(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,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'); % % scatter(1:n,data_ref(end-n+1:end),500,'Marker','x','LineWidth',1,'MarkerEdgeColor','red');
yticks(obj.trellis_states); % yticks(obj.trellis_states);
ylim([min(obj.trellis_states)-1 max(obj.trellis_states)+1]); % 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 end
VITERBI_ESTIMATION_SYMBOLS(1:length(data_in)) = first_sym(viterbi_path); VITERBI_ESTIMATION_SYMBOLS(1:length(data_in)) = first_sym(viterbi_path);
@@ -564,11 +657,6 @@ classdef MLSE < handle
end 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
end end

146
Classes/GifWriter.m Normal file
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@@ -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

6
Functions/EQ_structures/dsp_runid.m
View File

@@ -96,10 +96,10 @@ try
adaption= 1; adaption= 1;
use_dd_mode = 1; use_dd_mode = 1;
use_ffe = 1; use_ffe = 0;
use_dfe = 1; use_dfe = 0;
use_vnle_mlse = 1; use_vnle_mlse = 1;
use_dbtgt = 1; use_dbtgt = 0;
use_dbenc = 0; use_dbenc = 0;
addProcessingResultToDatabase = 0; addProcessingResultToDatabase = 0;

2
Functions/EQ_visuals/showEQNoisePSD.m
View File

@@ -43,6 +43,6 @@ end
end end
xlim([-eq_noise.fs/2* 1e-9 eq_noise.fs/2* 1e-9]); xlim([-eq_noise.fs/2* 1e-9 eq_noise.fs/2* 1e-9]);
ylim([-15, 0]); % ylim([-15, 0]);
end end

27
Functions/Theory/CCDM/ccdm_gpt_example.m Normal file
View File

@@ -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

13
Functions/beautifyBERplot.m
View File

@@ -8,12 +8,13 @@ function beautifyBERplot()
for i = 1:length(lines) for i = 1:length(lines)
lines(i).LineWidth = 1.3; % Thicker line width lines(i).LineWidth = 1.3; % Thicker line width
%lines(i).LineStyle = '-'; % Solid lines for simplicity lines(i).LineStyle = '-'; % Solid lines for simplicity
% if string(lines(i).Marker) == "none" if string(lines(i).Marker) == "none"
% lines(i).Marker = markers{mod(i-1, num_markers) + 1}; % Assign markers cyclically lines(i).Marker = markers{mod(i-1, num_markers) + 1}; % Assign markers cyclically
% end end
lines(i).MarkerSize = 4; % Marker size lines(i).MarkerSize = 7; % Marker size
lines(i).MarkerFaceColor = 'auto'; % Use line color for marker face lines(i).MarkerFaceColor = lines(i).Color; % Use line color for marker face
lines(i).MarkerEdgeColor = 'white';
end end
% Change all text interpreters to LaTeX % Change all text interpreters to LaTeX

16
projects/HighSpeedExperiment_2024/Auswertung_JLT/run_dsp_from_db.m
View File

@@ -40,18 +40,18 @@ end
fp = QueryFilter(); fp = QueryFilter();
% fp.where('Runs', 'run_id','EQUALS', 987); % fp.where('Runs', 'run_id','EQUALS', 987);
M = 6; M = 4;
% fp.where('Runs', 'pam_level','EQUALS', M); fp.where('Runs', 'pam_level','EQUALS', M);
% fp.where('Runs', 'bitrate','EQUALS', 480e9); fp.where('Runs', 'bitrate','EQUALS', 420e9);%360,390
% fp.where('Runs', 'symbolrate','EQUALS', 162e9); % fp.where('Runs', 'symbolrate','EQUALS', 162e9);
% fp.where('Runs', 'fiber_length','EQUALS', 1); fp.where('Runs', 'fiber_length','EQUALS', 2);
fp.where('Runs', 'is_mpi','EQUALS', 0); fp.where('Runs', 'is_mpi','EQUALS', 0);
% fp.where('Runs', 'interference_path_length','EQUALS', 1000); % fp.where('Runs', 'interference_path_length','EQUALS', 1000);
% fp.where('Runs', 'loop_id','GREATER_THAN', 11); % fp.where('Runs', 'loop_id','GREATER_THAN', 11);
% fp.where('Runs', 'sir','EQUALS',18); % fp.where('Runs', 'sir','EQUALS',18);
fp.where('Runs', 'wavelength','LESS_THAN', 1311); fp.where('Runs', 'wavelength','EQUAL', 1310);
% fp.where('Runs', 'db_mode','EQUALS', 0); fp.where('Runs', 'db_mode','EQUALS', 0);
% fp.where('Runs', 'rop_attenuation','NOT_EQUAL', 0); fp.where('Runs', 'rop_attenuation','EQUAL', 0);
% fp.where('Runs', 'power_pd_in','GREATER_THAN', 7); % fp.where('Runs', 'power_pd_in','GREATER_THAN', 7);
[dataTable,~] = db.queryDB(fp, db.getTableFieldNames('Runs')); [dataTable,~] = db.queryDB(fp, db.getTableFieldNames('Runs'));
@@ -69,7 +69,7 @@ wh.addStorage("dbenc_package");
% === RUN IT === % === RUN IT ===
[results,wh] = submitJobs(dataTable.run_id(:), dsp_options, "parallel", 'wh', wh, 'waitbar', true); [results,wh] = submitJobs(dataTable.run_id(:), dsp_options, "serial", 'wh', wh, 'waitbar', true);
results_db = results(dataTable.db_mode==1); results_db = results(dataTable.db_mode==1);
results_nodb = results(dataTable.db_mode==0); results_nodb = results(dataTable.db_mode==0);

2
projects/HighSpeedExperiment_2024/Auswertung_JLT/run_plot_measurements.m
View File

@@ -6,7 +6,7 @@ M = 4;
fp = QueryFilter(); fp = QueryFilter();
% fp.where('Runs', 'run_id','EQUALS', 987); % fp.where('Runs', 'run_id','EQUALS', 987);
fp.where('Runs', 'pam_level','EQUALS', M); fp.where('Runs', 'pam_level','EQUALS', M);
fp.where('Runs', 'symbolrate','EQUALS', 150e9); % fp.where('Runs', 'symbolrate','EQUALS', 150e9);
% fp.where('Runs', 'fiber_length','EQUALS', 10); % fp.where('Runs', 'fiber_length','EQUALS', 10);
fp.where('Runs', 'is_mpi','EQUALS', 0); fp.where('Runs', 'is_mpi','EQUALS', 0);
% fp.where('Runs', 'interference_path_length','EQUALS', 1000); % fp.where('Runs', 'interference_path_length','EQUALS', 1000);

220
projects/IMDD_base_system/simulation_bwl.m
View File

@@ -47,11 +47,12 @@ doub_mode = db_mode.no_db;
cols = linspecer(6); cols = linspecer(6);
rop = [-6]; rop = [-6];
bwl = [0.5:0.1:1.5]; bwl = [0.5:0.1:1.5];
fsym = [120:8:256].*1e9; fsym = [192:16:256].*1e9;
fsym =210e9; fsym = 208e9;
ber_vnle = []; ber_vnle = [];
ber_mlse = []; ber_mlse = [];
ber_mlse_burg = [];
ber_viterbi = []; ber_viterbi = [];
ber_db = []; ber_db = [];
ber_db_diff_precoded = []; ber_db_diff_precoded = [];
@@ -69,7 +70,7 @@ for r = 1:length(fsym)
apply_pulsef = 1; apply_pulsef = 1;
[Digi_sig,Symbols,Tx_bits] = PAMsource(... [Digi_sig,Symbols,Tx_bits] = PAMsource(...
"fsym",fsym(r),"M",M,"order",18,"useprbs",0,... "fsym",fsym(r),"M",M,"order",19,"useprbs",0,...
"fs_out",fdac,... "fs_out",fdac,...
"applyclipping",0,"clipfactor",1.5,... "applyclipping",0,"clipfactor",1.5,...
"applypulseform",apply_pulsef,"pulseformer",Pform,... "applypulseform",apply_pulsef,"pulseformer",Pform,...
@@ -95,15 +96,17 @@ for r = 1:length(fsym)
vbias = -u_pi*0.5; vbias = -u_pi*0.5;
[Opt_sig] = EML("mode",eml_mode.im_cosinus,"power",3,"fsimu",El_sig.fs,"lambda",laser_wavelength,"bias",vbias,"u_pi",u_pi,"linewidth",laser_linewidth,"randomkey",random_key+1).process(El_sig); [Opt_sig] = EML("mode",eml_mode.im_cosinus,"power",3,"fsimu",El_sig.fs,"lambda",laser_wavelength,"bias",vbias,"u_pi",u_pi,"linewidth",laser_linewidth,"randomkey",random_key+1).process(El_sig);
figure(15); if 0
hold on figure(15);
scatter(El_sig.signal(1:100000)+vbias,(abs(Opt_sig.signal(1:100000)).^2)*1e3,0.1,'.','DisplayName','Modulator TF') hold on
xlabel('Input in V') scatter(El_sig.signal(1:100000)+vbias,(abs(Opt_sig.signal(1:100000)).^2)*1e3,0.1,'.','DisplayName','Modulator TF')
ylabel('abs(Eopt)2 in mW','Interpreter','latex') xlabel('Input in V')
ylim([0 2]); ylabel('abs(Eopt)2 in mW','Interpreter','latex')
xlim([-3.2 0]); ylim([0 2]);
xlim([-3.2 0]);
Opt_sig.eye(fsym(r),M,"fignum",103837); Opt_sig.eye(fsym(r),M,"fignum",103837);
end
%%%%%% Fiber %%%%%% %%%%%% Fiber %%%%%%
Opt_sig = Fiber("fsimu",Opt_sig.fs,"fiber_length",link_length/1000,"alpha",0.3,"D",0,"lambda0",1310,"gamma",0,"Dslope",0.07).process(Opt_sig); Opt_sig = Fiber("fsimu",Opt_sig.fs,"fiber_length",link_length/1000,"alpha",0.3,"D",0,"lambda0",1310,"gamma",0,"Dslope",0.07).process(Opt_sig);
@@ -183,12 +186,11 @@ for r = 1:length(fsym)
Rx_sig = Scpe_cell{1}; Rx_sig = Scpe_cell{1};
Rx_sig = Rx_sig.normalize("mode","rms"); Rx_sig = Rx_sig.normalize("mode","rms");
if 0 if 1
%Duobinary Targeting %Duobinary Targeting
eq_ = EQ("Ne",[vnle_order1,vnle_order2,vnle_order3],"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",[vnle_order1,vnle_order2,vnle_order3],"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);
db_ref_sequence = Duobinary().encode(Symbols); db_ref_sequence = Duobinary().encode(Symbols);
db_ref_constellation = unique(db_ref_sequence.signal); db_ref_constellation = unique(db_ref_sequence.signal);
[eq_signal, eq_noise] = eq_.process(Rx_sig,db_ref_sequence); [eq_signal, eq_noise] = eq_.process(Rx_sig,db_ref_sequence);
@@ -197,14 +199,14 @@ for r = 1:length(fsym)
if viterbi if viterbi
mlse_ = MLSE_viterbi("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels); mlse_ = MLSE_viterbi("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
mlse_.DIR = [1,1]; mlse_.DIR = [1,1];
[mlse_sig_sd] = mlse_.process(eq_signal); [eq_signal_whitened] = mlse_.process(eq_signal);
else else
mlse_ = MLSE("DIR",[1,1],"duobinary_output",0,"M",M,"trellis_states",PAMmapper(M,0).get_levels ./ PAMmapper(M,0).get_scaling); mlse_ = MLSE("DIR",[1,1],"duobinary_output",0,"M",M,"trellis_states",PAMmapper(M,0).get_levels ./ PAMmapper(M,0).get_scaling);
mlse_.DIR = [1,1]; mlse_.DIR = [1,1];
[mlse_sig_sd,LLR,gmi_mlse_db(r)] = mlse_.process(eq_signal,Symbols); [eq_signal_whitened,LLR,gmi_mlse_db(r)] = mlse_.process(eq_signal,Symbols);
end end
mlse_sig_hd = PAMmapper(M,0,"eth_style",0).quantize(mlse_sig_sd); mlse_sig_hd = PAMmapper(M,0,"eth_style",0).quantize(eq_signal_whitened);
mlse_sig_hd_precoded = Duobinary().encode(mlse_sig_hd,"M",M); mlse_sig_hd_precoded = Duobinary().encode(mlse_sig_hd,"M",M);
mlse_sig_hd_precoded = Duobinary().decode(mlse_sig_hd_precoded,"M",M); mlse_sig_hd_precoded = Duobinary().decode(mlse_sig_hd_precoded,"M",M);
@@ -233,31 +235,29 @@ for r = 1:length(fsym)
eq_ = EQ("Ne",[vnle_order1,vnle_order2,vnle_order3],"Nb",[0,0,0],"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.00,"FFEmu",0,"plotfinal",0,"ideal_dfe",0); eq_ = EQ("Ne",[vnle_order1,vnle_order2,vnle_order3],"Nb",[0,0,0],"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.00,"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,"order",[50,2,2],"sps",2,"decide",0); % eq = VNLE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",[0.0004 0.0005 0.0006],"mu_tr",0,"order",[50,2,2],"sps",2,"decide",0);
[eq_signal_sd, eq_noise] = eq_.process(Rx_sig, Symbols); [eq_signal_fullresp, eq_noise] = eq_.process(Rx_sig, Symbols);
showEQNoisePSD(eq_noise, "fignum",1273876,"displayname",'noise after EQ'); showEQNoisePSD(eq_noise, "fignum",1273876,"displayname",'noise after EQ');
[mi_gomez(r)] = calc_air(eq_signal_sd, Symbols, "skip_front", 100, "skip_end", 100); [mi_gomez(r)] = calc_air(eq_signal_fullresp, Symbols, "skip_front", 100, "skip_end", 100);
[gmi_vnle_bitwise(r)] = calc_ngmi(eq_signal_sd,Symbols); [gmi_vnle_bitwise(r)] = calc_ngmi(eq_signal_fullresp,Symbols);
[gmi_bitwise_2(r)] = calc_gmi_bitwise(eq_signal_sd,Symbols); [gmi_bitwise_2(r)] = calc_gmi_bitwise(eq_signal_fullresp,Symbols);
snr_vnle(r) = calc_snr(Symbols, eq_signal_sd-Symbols); snr_vnle(r) = calc_snr(Symbols, eq_signal_fullresp-Symbols);
eq_signal_sd.plot("displayname",'bla','fignum',199);
eq_signal_sd.eye(fsym(r),M,"fignum",103837);
% eq_signal_fullresp.plot("displayname",'bla','fignum',199);
% eq_signal_fullresp.eye(fsym(r),M,"fignum",103837);
% Hard decision on VNLE output % Hard decision on VNLE output
eq_signal_hd = PAMmapper(M, 0).quantize(eq_signal_sd); eq_signal_hd = PAMmapper(M, 0).quantize(eq_signal_fullresp);
rx_bits = PAMmapper(M,0,"eth_style",0).demap(eq_signal_hd); rx_bits = PAMmapper(M,0,"eth_style",0).demap(eq_signal_hd);
[~,tot_err,ber_vnle(r),a] = calc_ber(rx_bits.signal,Tx_bits.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1); [~,tot_err,ber_vnle(r),a] = calc_ber(rx_bits.signal,Tx_bits.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
burst_vnle(r,:) = count_error_bursts(a, 10)./tot_err; burst_vnle(r,:) = count_error_bursts(a, 10)./tot_err;
showLevelConfusionMatrix(eq_signal_hd,Symbols,"M",M,"fignum",200,"displayname",'bla'); % showLevelConfusionMatrix(eq_signal_hd,Symbols,"M",M,"fignum",200,"displayname",'bla');
showLevelScatter(eq_signal_sd,Symbols,"displayname",'VNLE Out','f_sym',fsym(r),'fignum',201); % showLevelScatter(eq_signal_fullresp,Symbols,"displayname",'VNLE Out','f_sym',fsym(r),'fignum',201);
show2Dconstellation(eq_signal_sd,Symbols,"displayname",'VNLE Out','fignum',2241); % show2Dconstellation(eq_signal_fullresp,Symbols,"displayname",'VNLE Out','fignum',2241);
fprintf('BER VNLE: %.2e \n',ber_vnle(r)); fprintf('BER VNLE: %.2e \n',ber_vnle(r));
fprintf('NGMI VNLE: %.2f \n',gmi_vnle_bitwise(r)./m); fprintf('NGMI VNLE: %.2f \n',gmi_vnle_bitwise(r)./m);
if 1 if 1
% Process through postfilter and MLSE % Process through postfilter and MLSE
@@ -268,44 +268,132 @@ for r = 1:length(fsym)
pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1,"coefficients",[1,0.85]); pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1,"coefficients",[1,0.85]);
end end
mlse_ = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).get_levels ./ PAMmapper(M,0).get_scaling); % showEQNoisePSD(eq_noise,"postfilter_taps",pf_.coefficients,"displayname",'Postfilter Burg based');
[mlse_sig_sd,whitened_noise] = pf_.process(eq_signal_sd, eq_noise);
mlse_.DIR = pf_.coefficients;
alpha(r) = pf_.coefficients(2); alpha(r) = pf_.coefficients(2);
[signalclass_hd,LLR,gmi_mlse(r)] = mlse_.process(mlse_sig_sd,Symbols); alpha_vec = max(0,round(alpha(r),2)-0.2):0.025:round(alpha(r),2)+0.4;
mlse_sig_hd = PAMmapper(M, 0, "eth_style", 0).quantize(signalclass_hd); alpha_vec = unique(sort([alpha_vec, 1, alpha(r)]));
rx_bits = PAMmapper(M,0,"eth_style",0).demap(mlse_sig_hd);
[~,tot_err,ber_mlse(r),a] = calc_ber(rx_bits.signal,Tx_bits.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1); gmi_mlse_ = zeros(size(alpha_vec));
burst_mlse(r,:) = count_error_bursts(a, 10); ber_mlse_ = zeros(size(alpha_vec));
parfor a=1:numel(alpha_vec)
showLevelConfusionMatrix(mlse_sig_hd,Symbols,"M",M,"fignum",300,"displayname",'bla'); mlse_ = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).get_levels ./ PAMmapper(M,0).get_scaling,'DIR',[1,alpha_vec(a)]);
fprintf('BER MLSE: %.2e \n',ber_mlse(r)); pf_ = Postfilter("ncoeff",1,"useBurg",0,"coefficients",[1,alpha_vec(a)]);
fprintf('NGMI MLSE: %.5f \n',gmi_mlse(r)./m); [eq_signal_whitened,whitened_noise] = pf_.process(eq_signal_fullresp, eq_noise);
levels = sort(unique(Symbols.signal(:)).'); % 1×6 [signalclass_hd,LLR,gmi_mlse_(a)] = mlse_.process(eq_signal_whitened,Symbols);
pairs = reshape(mlse_sig_hd.signal,2,[]).';
isedge = ismember(pairs, [levels(1) levels(end)]);
isforbidden = sum(isedge,2)==2;
fprintf('Found %d forbidden transitions (even→odd edges).\n', nnz(isforbidden));
mlse_sig_hd = PAMmapper(M, 0, "eth_style", 0).quantize(signalclass_hd);
% Process through postfilter and MLSE rx_bits = PAMmapper(M,0,"eth_style",0).demap(mlse_sig_hd);
pf_ncoeffs = 1;
pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1); [~,tot_err,ber_mlse_(a),errpos] = calc_ber(rx_bits.signal,Tx_bits.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
mlse_ = MLSE_viterbi("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels); % burst_mlse(r,:) = count_error_bursts(errpos, 10);
[mlse_sig_sd,whitened_noise] = pf_.process(eq_signal_sd, eq_noise);
mlse_.DIR = pf_.coefficients; % if 0
mlse_sig_sd = mlse_.process(mlse_sig_sd); % fprintf('BER MLSE: %.2e \n',ber_mlse(r));
mlse_sig_hd = PAMmapper(M, 0, "eth_style", 0).quantize(mlse_sig_sd); % fprintf('NGMI MLSE: %.5f \n',gmi_mlse(r)./m);
rx_bits = PAMmapper(M,0,"eth_style",0).demap(mlse_sig_hd); %
[~,~,ber_viterbi(r),~] = calc_ber(rx_bits.signal,Tx_bits.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1); % showLevelConfusionMatrix(mlse_sig_hd,Symbols,"M",M,"fignum",300,"displayname",'bla');
fprintf('Viterbi BER: %.2e \n',ber_viterbi(r)); %
% levels = sort(unique(Symbols.signal(:)).'); % 1×6
% pairs = reshape(mlse_sig_hd.signal,2,[]).';
% isedge = ismember(pairs, [levels(1) levels(end)]);
% isforbidden = sum(isedge,2)==2;
% fprintf('Found %d forbidden transitions (even→odd edges).\n', nnz(isforbidden));
%
%
% % Process through postfilter and MLSE
% pf_ncoeffs = 1;
% pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
% mlse_ = MLSE_viterbi("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
% [eq_signal_whitened,whitened_noise] = pf_.process(eq_signal_fullresp, eq_noise);
% mlse_.DIR = pf_.coefficients;
% mlse_output = mlse_.process(eq_signal_whitened);
% mlse_sig_hd = PAMmapper(M, 0, "eth_style", 0).quantize(mlse_output);
% rx_bits = PAMmapper(M,0,"eth_style",0).demap(mlse_sig_hd);
% [~,~,ber_viterbi(r),~] = calc_ber(rx_bits.signal,Tx_bits.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
% fprintf('Viterbi BER: %.2e \n',ber_viterbi(r));
% end
end
[ber_mlse(r),idx] = min(ber_mlse_);
gmi_mlse(r) = gmi_mlse_(idx);
ber_mlse_burg(r) = ber_mlse_(alpha_vec==alpha(r));
best_alpha(r) = alpha_vec(idx);
end end
% IR target in EQ
if 1
% alpha_vec = max(0,round(alpha(r),2)-0.1):0.01:min(1,round(alpha(r),2)+0.1);
plot_stuff = 0;
gmi_mlse_pr_tgt_ = zeros(size(alpha_vec));
ber_mlse_pr_tgt_ = zeros(size(alpha_vec));
parfor a = 1:numel(alpha_vec)
eq_ = EQ("Ne",[vnle_order1,vnle_order2,vnle_order3],"Nb",[0,0,0],"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.00,"FFEmu",0,"plotfinal",0,"ideal_dfe",0);
Symbols_filt = Symbols.filter([1,alpha_vec(a)],1);
[eq_signal_prtgt, eq_noise] = eq_.process(Rx_sig, Symbols_filt);
if plot_stuff
% Plot the response for respective EQ targets
Symbols_filt.spectrum("displayname",'IDEAL Filtered Reference','fignum',240587);
eq_signal_whitened.spectrum("displayname",'Full tgt. EQ + PF','fignum',240587);
eq_signal_prtgt.spectrum("displayname",'Partial Resp. Target EQ','fignum',240587);
noise_pf_out = Symbols_filt-eq_signal_whitened;
noise_pr_tgt = Symbols_filt-eq_signal_prtgt;
noise_pf_out.spectrum("displayname",'Ideal PR - Whitening Out','fignum',240588);
noise_pr_tgt.spectrum("displayname",'Ideal PR - PR Target Out','fignum',240588);
end
mlse_ = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).get_levels ./ PAMmapper(M,0).get_scaling,'DIR',[1,alpha_vec(a)],'debug',0);
[signalclass_hd,LLR,gmi_mlse_pr_tgt_(a)] = mlse_.process(eq_signal_prtgt,Symbols);
mlse_sig_hd = PAMmapper(M, 0, "eth_style", 0).quantize(signalclass_hd);
rx_bits = PAMmapper(M,0,"eth_style",0).demap(mlse_sig_hd);
[~,tot_err,ber_mlse_pr_tgt_(a),errpos] = calc_ber(rx_bits.signal,Tx_bits.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
% burst_mlse_(a,:) = count_error_bursts(errpos, 10);
% fprintf('BER MLSE: %.2e \n',ber_mlse_pr_tgt_(a));
% fprintf('NGMI MLSE: %.5f \n',gmi_mlse_pr_tgt_(a)./m);
end
[ber_mlse_pr_tgt(r),idx] = min(ber_mlse_pr_tgt_);
gmi_mlse_pr_tgt(r) = gmi_mlse_pr_tgt_(idx);
best_alpha_pr_tgt(r) = alpha_vec(idx);
end
cols = cbrewer2('paired',8);
figure(); hold on
title(sprintf('%d GBd',fsym(r).*1e-9));
scatter(alpha_vec,ber_mlse_,15,'Marker','o','LineWidth',1,'DisplayName','MLSE','MarkerEdgeColor',cols(1,:));
scatter(best_alpha(r),ber_mlse(r),15,'Marker','o','LineWidth',2,'DisplayName','MLSE','MarkerEdgeColor',cols(2,:));
scatter(alpha(r),ber_mlse_burg(r),25,'Marker','+','LineWidth',2,'DisplayName','MLSE','MarkerEdgeColor',cols(2,:));
scatter(1,ber_db_diff_precoded(r),15,'Marker','diamond','LineWidth',2,'DisplayName','Duobinary','MarkerEdgeColor',cols(4,:));
scatter(1,ber_db(r),15,'Marker','diamond','LineWidth',2,'DisplayName','Duobinary','MarkerEdgeColor',cols(4,:));
scatter(alpha_vec,ber_mlse_pr_tgt_,15,'Marker','x','LineWidth',1,'DisplayName','MLSE Partial Resp tgt','MarkerEdgeColor',cols(5,:));
scatter(best_alpha_pr_tgt(r),ber_mlse_pr_tgt(r),25,'Marker','x','LineWidth',2,'DisplayName','MLSE','MarkerEdgeColor',cols(6,:));
set(gca,"YScale","log");
% ylim([1e-6 0.5]);
% xlim([0.1 1]);
drawnow;
end end
@@ -413,8 +501,8 @@ m = floor(log2(M)*10)/10;
figure(113+M); clf; hold on; figure(113+M); clf; hold on;
netrates_vnle = tp.calculateNetRate(fsym.* m, ... netrates_vnle = tp.calculateNetRate(fsym.* m, ...
'NGMI', gmi_vnle_bitwise./m, ... 'NGMI', gmi_vnle_bitwise./m, ...
'BER', ber_vnle); 'BER', ber_vnle);
% %
plot(xGHz, gmi_vnle_bitwise.*xGHz, ... plot(xGHz, gmi_vnle_bitwise.*xGHz, ...
'DisplayName','GMI*R VNLE', ... 'DisplayName','GMI*R VNLE', ...
@@ -435,8 +523,8 @@ plot(xGHz, gmi_mlse.*xGHz, ...
mk.MLSE{:}, 'LineStyle','-','LineWidth',LINE_WIDTH,'Color',cm.MLSE); mk.MLSE{:}, 'LineStyle','-','LineWidth',LINE_WIDTH,'Color',cm.MLSE);
netrates_mlse = tp.calculateNetRate(fsym.* m, ... netrates_mlse = tp.calculateNetRate(fsym.* m, ...
'NGMI', gmi_mlse./m, ... 'NGMI', gmi_mlse./m, ...
'BER', ber_mlse); 'BER', ber_mlse);
plot(xGHz, netrates_mlse.SDHD.NetRate.*1e-9, ... plot(xGHz, netrates_mlse.SDHD.NetRate.*1e-9, ...
'DisplayName','SD+HD MLSE', ... 'DisplayName','SD+HD MLSE', ...
mk.MLSE{:}, 'LineStyle',':','LineWidth',LINE_WIDTH,'Color',cm.MLSE); mk.MLSE{:}, 'LineStyle',':','LineWidth',LINE_WIDTH,'Color',cm.MLSE);
@@ -451,8 +539,8 @@ plot(xGHz, gmi_mlse_db.*xGHz, ...
mk.DB{:}, 'LineStyle','-','LineWidth',LINE_WIDTH,'Color',cm.DB); mk.DB{:}, 'LineStyle','-','LineWidth',LINE_WIDTH,'Color',cm.DB);
netrates_db = tp.calculateNetRate(fsym.* m, ... netrates_db = tp.calculateNetRate(fsym.* m, ...
'NGMI', gmi_mlse_db./m, ... 'NGMI', gmi_mlse_db./m, ...
'BER', ber_db); 'BER', ber_db);
plot(xGHz, netrates_db.SDHD.NetRate.*1e-9, ... plot(xGHz, netrates_db.SDHD.NetRate.*1e-9, ...
'DisplayName','SD+HD DB', ... 'DisplayName','SD+HD DB', ...
@@ -473,7 +561,7 @@ xlim([184, 256])
% Auxiliary nested helper for numerically stable log-sum-exp % Auxiliary nested helper for numerically stable log-sum-exp
function s = logsumexp(a) function s = logsumexp(a)
% LOGSUMEXP Compute log(sum(exp(a))) in a numerically stable way % LOGSUMEXP Compute log(sum(exp(a))) in a numerically stable way
m = max(a); m = max(a);
s = m + log(sum(exp(a - m))); s = m + log(sum(exp(a - m)));
end end

8
projects/IMDD_base_system/simulation_bwl_2.m
View File

@@ -136,9 +136,5 @@ eq_signal_hd = PAMmapper(M, 0).quantize(eq_signal_sd);
% Process through postfilter and MLSE % Process through postfilter and MLSE
[mlse_sig_sd,whitened_noise] = pf_.process(eq_signal_sd, eq_noise); [mlse_sig_sd,whitened_noise] = pf_.process(eq_signal_sd, eq_noise);
mlse_.DIR = pf_.coefficients; mlse_.DIR = pf_.coefficients;
mlse_sig_sd = mlse_.process(mlse_sig_sd,Symbols);
constellation = [-3, -1, 1, 3]; mlse_sig_hd = PAMmapper(M, 0, "eth_style", 0).quantize(mlse_sig_sd);
chatgpt_answer(mlse_sig_sd.signal, Symbols.signal,mlse_.DIR,constellation);
% mlse_sig_sd = mlse_.process(mlse_sig_sd,Symbols);
% mlse_sig_hd = PAMmapper(M, 0, "eth_style", options.eth_style_symbol_mapping).quantize(mlse_sig_sd);

194
projects/ML_based_MLSE/model.m Normal file
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@@ -0,0 +1,194 @@
%%% Run parameters
% TX
M = 4;
apply_pulsef = 1;
fdac = 256e9;
fadc = 256e9;
random_key = 2;
rcalpha = 0.05;
kover = 8;
vbias_rel = 0.5;
u_pi = 3.2;
vbias = -vbias_rel*u_pi;
laser_wavelength = 1310;
laser_linewidth = 1e6;
% Channel
link_length = 0;
alpha = 0;
doub_mode = db_mode.no_db;
cols = linspecer(6);
rop = [-6];
bwl = [0.5:0.1:1.5];
fsym = [208:16:256].*1e9;
% nonlin_mod = [0.5:0.01:0.75];
nonlin_mod = ones(size(fsym)).*0.5;
ffe_results = {};
mlse_results_lin= {};
for r = 1:length(fsym)
Pform = Pulseformer("fsym",fsym(r),"fdac",4*fsym(r),"pulse","rc","pulselength",16,"alpha",rcalpha);
db_precode = 0;
db_encode = 0;
duob_mode = db_mode.no_db;
apply_pulsef = 1;
[Digi_sig,Symbols,Tx_bits] = PAMsource(...
"fsym",fsym(r),"M",M,"order",17,"useprbs",0,...
"fs_out",fdac,...
"applyclipping",0,"clipfactor",1.5,...
"applypulseform",apply_pulsef,"pulseformer",Pform,...
"randkey",random_key,...
"db_precode",db_precode,"db_encode",db_encode,...
"mrds_code",0,"mrds_blocklength",512,"duobinary_mode",duob_mode).process();
El_sig = M8199B("kover",kover).process(Digi_sig);
%%%%% Electrical Driver Amplifier %%%%%%
El_sig = El_sig.normalize("mode","oneone");
%%%%% MODULATE E/O CONVERSION %%%%%
u_pi = 3.2;
vbias = -u_pi*nonlin_mod(r);
[Opt_sig] = EML("mode",eml_mode.im_cosinus,"power",3,"fsimu",El_sig.fs,"lambda",laser_wavelength,"bias",vbias,"u_pi",u_pi,"linewidth",laser_linewidth,"randomkey",random_key+1).process(El_sig);
%%%%%% Fiber %%%%%%
Opt_sig = Fiber("fsimu",Opt_sig.fs,"fiber_length",link_length/1000,"alpha",0.3,"D",0,"lambda0",1310,"gamma",0,"Dslope",0.07).process(Opt_sig);
%%%%%% ROP %%%%%%
Opt_sig = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power","amplification_db",rop).process(Opt_sig);
%%%%%% PD Square Law %%%%%%
PD_sig = Photodiode("fsimu",fdac*kover,"dark_current",2e-08,"responsivity",1,"temperature",20,"nep",1.8e-11,"randomkey",random_key).process(Opt_sig);
%%%%%% Low-pass RX (PD, El. Connectors and Scope %%%%%%
rx_bwl = 70e9;
PD_sig = Filter('filtdegree',4,"f_cutoff",rx_bwl,"fs",fdac*kover,"filterType",filtertypes.butterworth,"active",true).process(PD_sig);
% %%%%%% Low-pass Scope %%%%%%
Lp_scpe = Filter('filtdegree',4,"f_cutoff",110e9,"fs",fadc,"filterType",filtertypes.butterworth,"active",true);
%%%%%% Scope %%%%%%
Scpe_sig = Scope("fsimu",fdac*kover,"fadc",fadc,...
"delay",0,"fixed_delay",0,"filtertype",filtertypes.butterworth,...
"samplingdelay",0,"rand_samplingdelay",0,"freq_offset",0,"samp_jitter",0,...
"adcresolution",8,"quantbuffer",0.1,'block_dc',1,'lpf_active',1,'H_lpf',Lp_scpe).process(PD_sig);
Scpe_sig_2sps = Scpe_sig.resample("fs_out",2*fsym(r));
% 2sps
[~, Scpe_cell, ~, found_sync] = Scpe_sig_2sps.tsynch("reference", Symbols, "fs_ref", fsym(r), "debug_plots", 1);
Rx_sig_2sps = Scpe_cell{1};
Rx_sig_2sps = Rx_sig_2sps.normalize("mode","rms");
% 1sps
Scpe_sig_1sps = Scpe_sig.resample("fs_out",1*fsym(r));
[~, Scpe_cell_1sps, ~, found_sync] = Scpe_sig_1sps.tsynch("reference", Symbols, "fs_ref", fsym(r), "debug_plots", 1);
Rx_sig_1sps = Scpe_cell_1sps{1};
Rx_sig_1sps = Rx_sig_1sps.normalize("mode","rms");
%% Implement DSP directly here:
mu_lms = 0.0005;
tic
eq = ML_MLSE("epochs_tr",5,"epochs_dd",5,"len_tr",2^13,"mu_dd",mu_lms,"mu_tr",mu_lms,"order",50,"sps",2,"traceback_depth",32,"L",1);
[Eq_signal,Vit_signal] = eq.process(Rx_sig_2sps,Symbols);
toc
% tic
% eq = FFE_MLSE("epochs_tr",5,"epochs_dd",5,"len_tr",2^13,"mu_dd",mu_lms,"mu_tr",mu_lms,"order",50,"sps",2,"traceback_depth",32,"L",3);
% [Eq_signal,Vit_signal] = eq.process(Rx_sig_2sps,Symbols);
% toc
Eq_bits = PAMmapper(M, 0, "eth_style", 0).demap(Eq_signal);
[~, errors, ber, ~] = calc_ber(Eq_bits.signal, Tx_bits.signal, "skip_front", 0, "skip_end", 0, "returnErrorLocation", 1);
fprintf('FFE: %.2e \n',ber);
Vit_signal_ = Vit_signal;
Vit_signal_.signal = circshift(Vit_signal.signal,0);
Vit_bits = PAMmapper(M, 0, "eth_style", 0).demap(Vit_signal_);
[~, errors, ber, errpos] = calc_ber(Vit_bits.signal, Tx_bits.signal, "skip_front", 0, "skip_end", 0, "returnErrorLocation", 1);
fprintf('Viterbi: %.2e \n',ber);
%% optimize smth.
tr_len = 2.^[2:15];
tr_len = floor(tr_len);
ber = zeros(size(tr_len));
parfor m = 1:numel(tr_len)
mu_lms = 0.0005;
eq = ML_MLSE("epochs_tr",5,"epochs_dd",5,"len_tr",2^13,"mu_dd",mu_lms,"mu_tr",mu_lms,"order",50,"sps",2,"traceback_depth",tr_len(m));
[Eq_signal,Vit_signal] = eq.process(Rx_sig_2sps,Symbols);
% Eq_bits = PAMmapper(M, 0, "eth_style", 0).demap(Eq_signal);
% [~, errors, ber(m), ~] = calc_ber(Eq_bits.signal, Tx_bits.signal, "skip_front", 0, "skip_end", 0, "returnErrorLocation", 1);
Vit_bits = PAMmapper(M, 0, "eth_style", 0).demap(Vit_signal);
[~, errors, ber(m), errpos] = calc_ber(Vit_bits.signal, Tx_bits.signal, "skip_front", 0, "skip_end", 0, "returnErrorLocation", 1);
end
figure(5); hold on
title('1-SPS')
plot(tr_len,ber,'DisplayName','BER');
xlabel('Traceback Length')
beautifyBERplot
legend
ylim([1e-4, 1e-1]);
set(gca,'YScale','log');
%% RUN Comparison
len_tr = 4096*2;
mu_ffe1 = 0.0001;
mu_ffe2 = 0.0008;
mu_ffe3 = 0.001;
mu_dc = 0.005;
% mu_dc = 0;
mu_ffe = [mu_ffe1 mu_ffe2 mu_ffe3];
mu_dfe = 0.0004;
pf_ncoeffs = 1;
ffe_order = [50, 0, 0];
mu_lms = 0.0005;
eq_ = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",2^13,"mu_dd",mu_lms,"mu_tr",mu_lms,"order",50,"sps",1,"dd_mode",1,"adaption_technique","lms");
% eq_ = EQ("Ne",ffe_order,"Nb",[0,0,0],"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",1,"DCmu",0.00,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
pf_ = Postfilter("ncoeff",pf_ncoeffs,"useBurg",1);
mlse_ = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels,'scale_mode',0,'trellis_exclusion',0,'trellis_state_mode',2,'debug',0);
[ffe_results{r}, mlse_results_lin{r}] = vnle_postfilter_mlse(eq_, pf_, mlse_, M, Rx_sig_1sps, Symbols, Tx_bits, ...
"precode_mode", duob_mode,...
'showAnalysis', 0, ...
"postFFE", [],...
"eth_style_symbol_mapping", 0);
mlse_results_lin{r}.metrics.print;
ffe_results{r}.metrics.print;
end

229
projects/Nonlinear_MLSE/simulation_nonlin_dsp.m Normal file
View File

@@ -0,0 +1,229 @@
%%% Run parameters
% TX
M = 4;
m = floor(log2(M)*10)/10;
fsym = 224e9;
apply_pulsef = 1;
fdac = 256e9;
fadc = 256e9;
random_key = 2;
rcalpha = 0.05;
kover = 8;
vbias_rel = 0.5;
u_pi = 3.2;
vbias = -vbias_rel*u_pi;
laser_wavelength = 1310;
laser_linewidth = 1e6;
% Channel
link_length = 0;
vnle_order1 = 50;
vnle_order2 = 0;
vnle_order3 = 0;
vnle_order=[vnle_order1,vnle_order2,vnle_order3];
dfe_order = [0 0 0];
alpha = 0;
len_tr = 4096*2;
mu_ffe1 = 0.0001;
mu_ffe2 = 0.0008;
mu_ffe3 = 0.001;
mu_dc = 0.005;
% mu_dc = 0;
mu_ffe = [mu_ffe1 mu_ffe3 mu_ffe3];
mu_dfe = 0.0004;
dfe_ = sum(dfe_order)>0;
doub_mode = db_mode.no_db;
cols = linspecer(6);
rop = [-8];
bwl = [0.5:0.1:1.5];
fsym = [208:16:256].*1e9;
nonlin_mod = [0.5:0.01:0.75];
fsym = ones(size(nonlin_mod)).*fsym(1);
ffe_results = {};
mlse_results_lin= {};
vnle_results= {};
mlse_results_nonlin= {};
mlse_results_nonlin_states= {};
g_eye = GifWriter('Name','eye','Parallel',true);
g_mod = GifWriter('Name','modulator','Parallel',true);
for r = 1:length(nonlin_mod)
Pform = Pulseformer("fsym",fsym(r),"fdac",4*fsym(r),"pulse","rc","pulselength",16,"alpha",rcalpha);
db_precode = 0;
db_encode = 0;
duob_mode = db_mode.no_db;
apply_pulsef = 1;
[Digi_sig,Symbols,Tx_bits] = PAMsource(...
"fsym",fsym(r),"M",M,"order",19,"useprbs",0,...
"fs_out",fdac,...
"applyclipping",0,"clipfactor",1.5,...
"applypulseform",apply_pulsef,"pulseformer",Pform,...
"randkey",random_key,...
"db_precode",db_precode,"db_encode",db_encode,...
"mrds_code",0,"mrds_blocklength",512,"duobinary_mode",duob_mode).process();
% El_sig = AWG("fdac",fdac,"f_cutoff",fsym(r),"lpf_active",0,"kover",kover,"bit_resolution",12,"upsampling_method","samplehold","precomp_sinc_rolloff",0).process(Digi_sig);
El_sig = M8199B("kover",kover).process(Digi_sig);
% AWG("fdac",fdac,"f_cutoff",fsym(r),"lpf_active",0,"kover",kover,"bit_resolution",12,"upsampling_method","samplehold","precomp_sinc_rolloff",0).process(Digi_sig);
%%%%% Low-pass el. components %%%%%%
% tx_bwl = 100e9;
% El_sig = Filter('filtdegree',3,"f_cutoff",tx_bwl,"fs",fdac*kover,"filterType",filtertypes.butterworth,"active",true).process(El_sig);
%%%%% Electrical Driver Amplifier %%%%%%
El_sig = El_sig.normalize("mode","oneone");
% El_sig = El_sig.setPower(1,"dBm");
% figure;histogram(El_sig.signal);
%%%%% MODULATE E/O CONVERSION %%%%%
u_pi = 3.2;
vbias = -u_pi*nonlin_mod(r);
[Opt_sig] = EML("mode",eml_mode.im_cosinus,"power",3,"fsimu",El_sig.fs,"lambda",laser_wavelength,"bias",vbias,"u_pi",u_pi,"linewidth",laser_linewidth,"randomkey",random_key+1).process(El_sig);
if 1
figure(15);
hold on
scatter(El_sig.signal(1:100000)+vbias,(abs(Opt_sig.signal(1:100000)).^2)*1e3,0.1,'.','DisplayName','Modulator TF')
xlabel('Input in V')
ylabel('abs(Eopt)2 in mW','Interpreter','latex')
ylim([0 2]);
xlim([-3.2 0]);
g_mod.addFrame(15, r);
Opt_sig.eye(fsym(r), M, "fignum", 103837);
g_eye.addFrame(103837, r);
end
%%%%%% Fiber %%%%%%
Opt_sig = Fiber("fsimu",Opt_sig.fs,"fiber_length",link_length/1000,"alpha",0.3,"D",0,"lambda0",1310,"gamma",0,"Dslope",0.07).process(Opt_sig);
%%%%%% ROP %%%%%%
Opt_sig = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power","amplification_db",rop).process(Opt_sig);
%%%%%% PD Square Law %%%%%%
PD_sig = Photodiode("fsimu",fdac*kover,"dark_current",2e-08,"responsivity",1,"temperature",20,"nep",1.8e-11,"randomkey",random_key).process(Opt_sig);
%%%%%% Low-pass RX (PD, El. Connectors and Scope %%%%%%
rx_bwl = 70e9;
PD_sig = Filter('filtdegree',4,"f_cutoff",rx_bwl,"fs",fdac*kover,"filterType",filtertypes.butterworth,"active",true).process(PD_sig);
% %%%%%% Low-pass Scope %%%%%%
Lp_scpe = Filter('filtdegree',4,"f_cutoff",110e9,"fs",fadc,"filterType",filtertypes.butterworth,"active",true);
%%%%%% Scope %%%%%%
Scpe_sig = Scope("fsimu",fdac*kover,"fadc",fadc,...
"delay",0,"fixed_delay",0,"filtertype",filtertypes.butterworth,...
"samplingdelay",0,"rand_samplingdelay",0,"freq_offset",0,"samp_jitter",0,...
"adcresolution",8,"quantbuffer",0.1,'block_dc',1,'lpf_active',1,'H_lpf',Lp_scpe).process(PD_sig);
Scpe_sig_2sps = Scpe_sig.resample("fs_out",2*fsym(r));
% Scpe_sig_resampled.signal = Scpe_sig_resampled.signal(1:2*length(Symbols));
[~, Scpe_cell, ~, found_sync] = Scpe_sig_2sps.tsynch("reference", Symbols, "fs_ref", fsym(r), "debug_plots", 0);
Rx_sig = Scpe_cell{1};
Rx_sig = Rx_sig.normalize("mode","rms");
if 1
%% FFE
% ffe_order = [50, 0, 0];
% eq_ffe = EQ("Ne",ffe_order,"Nb",[0,0,0],"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);
%
% ffe_results = ffe(eq_ffe,M,Rx_sig,Symbols,Tx_bits,...
% "precode_mode",duob_mode,...
% 'showAnalysis',0,...
% "postFFE",[],...
% "eth_style_symbol_mapping",0);
%
% ffe_results.metrics.print;
% ffe_results.config.equalizer_structure = "ffe";
%
%% MLSE linear
pf_ncoeffs = 1;
ffe_order = [50, 0, 0];
eq_ = EQ("Ne",ffe_order,"Nb",[0,0,0],"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("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels,'scale_mode',2,'trellis_exclusion',0,'trellis_state_mode',2,'debug',1);
[ffe_results{r}, mlse_results_lin{r}] = vnle_postfilter_mlse(eq_, pf_, mlse_, M, Rx_sig, Symbols, Tx_bits, ...
"precode_mode", duob_mode,...
'showAnalysis', 0, ...
"postFFE", [],...
"eth_style_symbol_mapping", 0);
mlse_results_lin{r}.metrics.print;
ffe_results{r}.metrics.print;
%% MLSE nonlinear pre
pf_ncoeffs = 1;
ffe_order = [50, 1, 0];
eq_ = EQ("Ne",ffe_order,"Nb",[0,0,0],"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("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels,'scale_mode',2,'trellis_exclusion',0,'trellis_state_mode',2);
[vnle_results{r}, mlse_results_nonlin{r}] = vnle_postfilter_mlse(eq_, pf_, mlse_, M, Rx_sig, Symbols, Tx_bits, ...
"precode_mode", duob_mode,...
'showAnalysis', 0, ...
"postFFE", [],...
"eth_style_symbol_mapping", 0);
mlse_results_nonlin{r}.metrics.print;
vnle_results{r}.metrics.print;
%% nonlinear states MLSE linear pre
pf_ncoeffs = 1;
ffe_order = [50, 0, 0];
eq_ = EQ("Ne",ffe_order,"Nb",[0,0,0],"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("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels,'scale_mode',2,'trellis_exclusion',0,'trellis_state_mode',3);
[~, mlse_results_nonlin_states{r}] = vnle_postfilter_mlse(eq_, pf_, mlse_, M, Rx_sig, Symbols, Tx_bits, ...
"precode_mode", duob_mode,...
'showAnalysis', 0, ...
"postFFE", [],...
"eth_style_symbol_mapping", 0);
mlse_results_nonlin_states{r}.metrics.print;
end
end
g_mod.compile(15);
g_eye.compile(103837);
%%
figure();hold on;
plot(nonlin_mod,cellfun(@(x) x.metrics.BER, ffe_results),'DisplayName','FFE')
plot(nonlin_mod,cellfun(@(x) x.metrics.BER, vnle_results),'DisplayName','VNLE')
plot(nonlin_mod,cellfun(@(x) x.metrics.BER, mlse_results_lin),'DisplayName','FFE+MLSE')
plot(nonlin_mod,cellfun(@(x) x.metrics.BER, mlse_results_nonlin_states),'DisplayName','FFE+nonlin. states MLSE')
plot(nonlin_mod,cellfun(@(x) x.metrics.BER, mlse_results_nonlin),'DisplayName','VNLE+MLSE')
xlabel('Nonlinear Driving');
ylabel('BER')
set(gca,'YScale','log');
legend;
ylim([1e-4 1e-1]);
beautifyBERplot;