Merge branch 'main' of cau-git.rz.uni-kiel.de:nt/mitarbeiter/silas/imdd_simulation
This commit is contained in:
sutef391
252
projects/WDM/WDM_auswertung.m
Normal file
252
projects/WDM/WDM_auswertung.m
Normal file
@@ -0,0 +1,252 @@
|
||||
|
||||
|
||||
try
|
||||
rop = res.settings.rop; % 12 points
|
||||
wavelengthplan = res.settings.wavelengthplan;
|
||||
catch
|
||||
wavelengthplan = [1295,1305,1315,1325];
|
||||
wavelengthplan = calcWavelengthPlan(16,400e9,1310);
|
||||
rop = -8.25:0.75:0;
|
||||
end
|
||||
|
||||
N = length(wavelengthplan);
|
||||
figure(); hold on;
|
||||
cols = cbrewer2('set2',N); % one color per wavelength (Ch)
|
||||
|
||||
fec = 2.2e-4;
|
||||
fec = 3.8e-3;
|
||||
Sffe = cell(1,N);
|
||||
Svnle = cell(1,N);
|
||||
Smlse = cell(1,N);
|
||||
Sdbt = cell(1,N);
|
||||
|
||||
% Choose your quantile band. For your old style, use 0.04/0.99:
|
||||
qLow = 0.0; % lower quantile (e.g., 0.04 for old script)
|
||||
qHigh = 1; % upper quantile (e.g., 0.99 for old script)
|
||||
cols = linspecer(N); % one color per wavelength (Ch)
|
||||
cols = cbrewer2('set1',N);
|
||||
|
||||
for l = 1:N
|
||||
% Slice 12x50 cell arrays
|
||||
ffe_cells = reshape(squeeze(res.ffe(l,:,:)),length(rop),[]);
|
||||
vnle_cells = reshape(squeeze(res.vnle(l,:,:)),length(rop),[]);
|
||||
mlse_cells = reshape(squeeze(res.mlse(l,:,:)),length(rop),[]);
|
||||
dbt_cells = reshape(squeeze(res.dbt(l,:,:)),length(rop),[]);
|
||||
|
||||
[Sffe{l}, noX_ffe] = fecCrossings(rop, ffe_cells, fec);
|
||||
|
||||
[Svnle{l}, noX_ffe] = fecCrossings(rop, vnle_cells, fec);
|
||||
|
||||
[Smlse{l}, noX_ffe] = fecCrossings(rop, mlse_cells, fec);
|
||||
|
||||
[Sdbt{l}, noX_ffe] = fecCrossings(rop, dbt_cells, fec);
|
||||
|
||||
% Extract BER matrices using only complete realizations (12/12 ROP filled)
|
||||
ffe_mat = extractCompleteBER(ffe_cells); % 12 x K_ffe
|
||||
vnle_mat = extractCompleteBER(vnle_cells); % 12 x K_vnle
|
||||
mlse_mat = extractCompleteBER(mlse_cells); % 12 x K_mlse
|
||||
mlse_alpha_mat = extractCompleteAlphas(mlse_cells); % 12 x K_mlse
|
||||
dbt_mat = extractCompleteBER(dbt_cells); % 12 x K_dbt
|
||||
|
||||
showLegend = 1; % one legend entry per technique
|
||||
|
||||
% Plot shaded band + mean line with boundedline
|
||||
% plotBandMeanBL(rop, ffe_mat, cols(l,:), sprintf('FFE @ %.1d nm',round(wavelengthplan(l))), qLow, qHigh, '--s', showLegend);
|
||||
% scatter(Sffe,fec.*ones(size(Sffe)),20,'v','MarkerFaceColor','black');
|
||||
|
||||
plotBandMeanBL(rop, vnle_mat, cols(l,:), sprintf('VNLE @ %.1d nm',round(wavelengthplan(l))), qLow, qHigh, '--x', showLegend);
|
||||
|
||||
% plotBandMeanBL(rop, mlse_mat, cols(l,:), sprintf('VNLE+PF+MLSE @ %.1d nm',round(wavelengthplan(l))), qLow, qHigh, '-o', showLegend);
|
||||
|
||||
% plotBandMeanBL(rop, dbt_mat, cols(l,:), sprintf('DBt.+MLSE @ %.1d nm',round(wavelengthplan(l))), qLow, qHigh, '--v', showLegend);
|
||||
|
||||
set(gca,'XScale','linear','YScale','log','TickLabelInterpreter','latex','FontSize',11);
|
||||
yline([3.8e-3, 2.2e-4], 'HandleVisibility','off','LineWidth',1.5);
|
||||
|
||||
end
|
||||
|
||||
ylabel('BER');
|
||||
xlabel('ROP');
|
||||
title('BER vs. ROP');
|
||||
xlim([min(rop) max(rop)]);
|
||||
ylim([1e-5 0.3]);
|
||||
grid on;
|
||||
legend show;
|
||||
|
||||
|
||||
S_cell = Sdbt;
|
||||
S_cell =Smlse;
|
||||
S_cell = {Svnle,Smlse,Sdbt};
|
||||
S_cell = {Svnle};
|
||||
figure(5); hold on;
|
||||
for i = 1:length(S_cell)
|
||||
% Pad to rectangular matrix: rows = realizations, cols = wavelengths
|
||||
Kmax = max(cellfun(@numel, S_cell{i}));
|
||||
S_mat = NaN(Kmax, N);
|
||||
for l = 1:N
|
||||
k = numel(S_cell{i}{l});
|
||||
if k > 0
|
||||
S_mat(1:k, l) = S_cell{i}{l};
|
||||
end
|
||||
end
|
||||
|
||||
% --- Violin plot over wavelengths (columns) ---
|
||||
|
||||
cols=linspecer(3);
|
||||
catLabels = arrayfun(@(nm) sprintf('%d nm', nm), wavelengthplan, 'UniformOutput', false);
|
||||
vs = violinplot(S_mat, catLabels, ...
|
||||
'ViolinColor', cols(i,:), ...
|
||||
'ViolinAlpha', 0.10, ...
|
||||
'MarkerSize', 20, ...
|
||||
'ShowMedian', true, ...
|
||||
'EdgeColor', cols(i,:), ...
|
||||
'ShowWhiskers', false, ...
|
||||
'ShowData', true, ...
|
||||
'ShowBox', false, ...
|
||||
'Bandwidth', 0.05);
|
||||
|
||||
ylim([floor(min(S_mat,[],'all')), ceil(max(S_mat,[],'all'))])
|
||||
ylim([-8 0]);
|
||||
ylabel('ROP at FEC crossing');
|
||||
title(sprintf('RROP to cross BER %.2e', fec));
|
||||
grid on; box on;
|
||||
|
||||
end
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
%% ================= helper =================
|
||||
function plotBandMeanBL(x, Y, color, techLabel, qLow, qHigh, lineSpec, showLegend)
|
||||
% Y: (nPoints x nRealizations)
|
||||
% Remove realizations that are entirely zero (like removeZeros behavior)
|
||||
badCols = all(Y == 0, 1);
|
||||
Y(:, badCols) = [];
|
||||
|
||||
Y(Y==0) = 1e-8;
|
||||
% Stats across realizations
|
||||
mu = mean(Y, 2, 'omitnan'); % mean line
|
||||
lo = quantile(Y, qLow, 2); % lower bound
|
||||
hi = quantile(Y, qHigh, 2); % upper bound
|
||||
|
||||
% Convert to asymmetric distances required by boundedline:
|
||||
% b(:,1) = distance to lower side; b(:,2) = distance to upper side
|
||||
b = [mu - lo, hi - mu];
|
||||
|
||||
% Call boundedline with alpha shading
|
||||
[hl, hp] = boundedline(x(:), mu(:), b, lineSpec, 'alpha', ...
|
||||
'transparency', 0.18);
|
||||
% Color styling
|
||||
set(hl, 'Color', color, 'LineWidth', 1.4, 'MarkerSize', 4);
|
||||
set(hp, 'FaceColor', color, 'HandleVisibility','off'); % patch hidden in legend
|
||||
|
||||
% Single legend entry per technique (use first wavelength only)
|
||||
if showLegend
|
||||
set(hl, 'DisplayName', techLabel);
|
||||
else
|
||||
set(hl, 'HandleVisibility','off');
|
||||
end
|
||||
|
||||
% Optional: outline the bounds if outlinebounds is available
|
||||
if exist('outlinebounds','file') == 2
|
||||
ho = outlinebounds(hl, hp);
|
||||
set(ho, 'linestyle', ':', 'color', color, 'linewidth', 1, ...
|
||||
'HandleVisibility','off');
|
||||
end
|
||||
end
|
||||
|
||||
function [S, noCrossingMask, Y_keep] = fecCrossings(rop, cells12xR, fec)
|
||||
% cells12xR: 12xR cell array (one wavelength + scheme slice)
|
||||
% each cell must be a struct with .metrics.BER
|
||||
% rop: 12x1 numeric vector of ROP points
|
||||
% fec: scalar FEC threshold (e.g., 3.8e-3)
|
||||
%
|
||||
% Outputs:
|
||||
% S 1xK vector of crossing ROP per kept realization (NaN if none)
|
||||
% noCrossingMask 1xK logical mask: true if no crossing for that realization
|
||||
% Y_keep 12xK numeric BER matrix used for the crossing detection
|
||||
|
||||
% 1) keep only complete realization columns
|
||||
Y = extractCompleteBER(cells12xR); % -> 12 x K
|
||||
if isempty(Y)
|
||||
S = [];
|
||||
noCrossingMask = [];
|
||||
Y_keep = Y;
|
||||
return;
|
||||
end
|
||||
|
||||
% 2) optionally drop realizations with mean BER > 0.1
|
||||
ok = mean(Y,1,'omitnan') <= 0.1;
|
||||
Y = Y(:, ok);
|
||||
if isempty(Y)
|
||||
S = [];
|
||||
noCrossingMask = [];
|
||||
Y_keep = Y;
|
||||
return;
|
||||
end
|
||||
|
||||
% 3) find crossings per realization
|
||||
nR = size(Y,2);
|
||||
S = nan(1,nR);
|
||||
noCrossingMask = true(1,nR);
|
||||
|
||||
rop = rop(:); % ensure column
|
||||
for j = 1:nR
|
||||
y = Y(:,j);
|
||||
|
||||
% sign change from >fec to <=fec (first time it drops below FEC)
|
||||
above = (y > fec);
|
||||
idx = find(above(1:end-1) & ~above(2:end), 1, 'first');
|
||||
|
||||
if ~isempty(idx)
|
||||
% linear interpolation between (x1,y1) and (x2,y2)
|
||||
x1 = rop(idx); y1 = y(idx);
|
||||
x2 = rop(idx+1); y2 = y(idx+1);
|
||||
|
||||
if isfinite(y1) && isfinite(y2) && y2 ~= y1
|
||||
t = (fec - y1) / (y2 - y1);
|
||||
S(j) = x1 + t*(x2 - x1);
|
||||
noCrossingMask(j) = false;
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
Y_keep = Y;
|
||||
end
|
||||
|
||||
|
||||
|
||||
function Y = extractCompleteBER(cellSlice)
|
||||
% cellSlice: 12xR cell array; each cell should be a struct with .metrics.BER
|
||||
% Keep only those realization columns where ALL 12 ROP entries are valid.
|
||||
if isempty(cellSlice), Y = []; return; end
|
||||
nR = size(cellSlice,2);
|
||||
keep = false(1,nR);
|
||||
for r = 1:nR
|
||||
col = cellSlice(:,r);
|
||||
keep(r) = all(cellfun(@(c) ~isempty(c) , col));
|
||||
end
|
||||
if ~any(keep), Y = []; return; end
|
||||
Y = cellfun(@(c) c.metrics.BER, cellSlice(:,keep), 'UniformOutput', true);
|
||||
end
|
||||
|
||||
function Y = extractCompleteAlphas(cellSlice)
|
||||
% cellSlice: 12xR cell array; each cell should be a struct with .metrics.BER
|
||||
% Keep only those realization columns where ALL 12 ROP entries are valid.
|
||||
if isempty(cellSlice), Y = []; return; end
|
||||
nR = size(cellSlice,2);
|
||||
keep = false(1,nR);
|
||||
for r = 1:nR
|
||||
col = cellSlice(:,r);
|
||||
keep(r) = all(cellfun(@(c) ~isempty(c) , col));
|
||||
end
|
||||
if ~any(keep), Y = []; return; end
|
||||
Y = cellfun(@(c) c.metrics.Alpha, cellSlice(:,keep), 'UniformOutput', true);
|
||||
end
|
||||
|
||||
@@ -15,9 +15,9 @@ m = floor(log2(M)*10)/10;
|
||||
fsym = 224e9;
|
||||
fdac = 2*fsym;
|
||||
fadc = 2*fsym;
|
||||
random_key = 2;
|
||||
s.random_key = 100;
|
||||
|
||||
% Laser / Modulator
|
||||
% Laser / s.Modulator
|
||||
vbias_rel = 0.5;
|
||||
u_pi = 3.2;
|
||||
vbias = -vbias_rel*u_pi;
|
||||
@@ -47,8 +47,8 @@ apply_pulsef = 0;
|
||||
rcalpha = 0.05;
|
||||
Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rc","pulselength",16,"alpha",rcalpha);
|
||||
|
||||
N = numel(wavelengthplan);
|
||||
f_plan = physconst('lightspeed')./(wavelengthplan.*1e-9);
|
||||
N = numel(s.wavelengthplan);
|
||||
f_plan = physconst('lightspeed')./(s.wavelengthplan.*1e-9);
|
||||
margin = 25e12; % some THz left and right
|
||||
f_span = (max(f_plan)+margin)-(min(f_plan)-margin);
|
||||
f_nyq = f_span/2;
|
||||
@@ -57,38 +57,38 @@ upsample_required = f_nyq./(fdac*kover/2);
|
||||
upsample_pow = 2^nextpow2(upsample_required);
|
||||
upsample_ceil = ceil(upsample_required);
|
||||
|
||||
f_opt = fdac*kover*upsample_pow;
|
||||
f_opt_nyq = f_opt/2;
|
||||
s.f_opt = fdac*kover*upsample_pow;
|
||||
s.f_opt_nyq = s.f_opt/2;
|
||||
|
||||
signal_cell = {};
|
||||
Symbols = {};
|
||||
Tx_bits = {};
|
||||
|
||||
rop = -8.25:0.75:0;
|
||||
s.rop = -6:0.75:-0.75;
|
||||
|
||||
output_ffe = cell(length(wavelengthplan),length(rop),num_realiz);
|
||||
output_vnle = cell(length(wavelengthplan),length(rop),num_realiz);
|
||||
output_mlse = cell(length(wavelengthplan),length(rop),num_realiz);
|
||||
output_dbt = cell(length(wavelengthplan),length(rop),num_realiz);
|
||||
output_ffe = cell(length(s.wavelengthplan),length(s.rop),s.num_realiz);
|
||||
output_vnle = cell(length(s.wavelengthplan),length(s.rop),s.num_realiz);
|
||||
output_mlse = cell(length(s.wavelengthplan),length(s.rop),s.num_realiz);
|
||||
output_dbt = cell(length(s.wavelengthplan),length(s.rop),s.num_realiz);
|
||||
|
||||
for realiz = 1:num_realiz
|
||||
for realiz = 1:s.num_realiz
|
||||
|
||||
|
||||
parfor l = 1:N
|
||||
|
||||
[Digi_sig,Symbols{l},Tx_bits{l}] = PAMsource(...
|
||||
"fsym",fsym,"M",M,"order",18,"useprbs",0,...
|
||||
"fsym",fsym,"M",s.M,"order",18,"useprbs",0,...
|
||||
"fs_out",fdac,...
|
||||
"applyclipping",0,"clipfactor",1.5,...
|
||||
"applypulseform",apply_pulsef,"pulseformer",Pform,...
|
||||
"randkey",random_key+l+realiz,...
|
||||
"randkey",s.random_key+l+realiz,...
|
||||
"db_precode",db_precode,"db_encode",db_encode,...
|
||||
"mrds_code",0,"mrds_blocklength",512,"duobinary_mode",duob_mode).process();
|
||||
|
||||
% Digi_sig.spectrum("fignum",101,"displayname",'bla','normalizeTo0dB',0,'lambda0_nm',1310,'useWavelengthAxis',0);
|
||||
Lp_awg = Filter('filtdegree',3,"f_cutoff",100e9,"fs",fdac*kover,"filterType",filtertypes.gaussian,"active",true);
|
||||
El_sig = AWG("fdac",fdac,"f_cutoff",fsym,"lpf_active",1,"kover",kover,"bit_resolution",12,"upsampling_method","samplehold","precomp_sinc_rolloff",0,"H_lpf",Lp_awg,"dac_max",0.6,"dac_min",-0.6).process(Digi_sig);
|
||||
% El_sig = M8199B("kover",kover).process(Digi_sig);
|
||||
% El_sig = s.M8199B("kover",kover).process(Digi_sig);
|
||||
% El_sig.spectrum("fignum",101,"displayname",'bla','normalizeTo0dB',0,'lambda0_nm',1310,'useWavelengthAxis',0);
|
||||
|
||||
%%%%% Electrical Driver Amplifier %%%%%%
|
||||
@@ -96,10 +96,10 @@ for realiz = 1:num_realiz
|
||||
% El_sig = El_sig.setPower(1,"dBm");
|
||||
% figure;histogram(El_sig.signal);
|
||||
|
||||
%%%%% MODULATE E/O CONVERSION %%%%%
|
||||
Eml_out = EML("mode",eml_mode.im_cosinus,"power",3,"fsimu",El_sig.fs,"lambda",wavelengthplan(l),"bias",vbias,"u_pi",u_pi,"linewidth",laser_linewidth,"randomkey",random_key+l+realiz).process(El_sig);
|
||||
%%%%% s.MODULATE E/O CONVERSION %%%%%
|
||||
Eml_out = EML("mode",eml_mode.im_cosinus,"power",3,"fsimu",El_sig.fs,"lambda",s.wavelengthplan(l),"bias",vbias,"u_pi",u_pi,"linewidth",laser_linewidth,"randomkey",s.random_key+l+realiz).process(El_sig);
|
||||
|
||||
signal_cell{l} = Polarization_Controller("mode","rot_power","desired_power",30).process(Eml_out);
|
||||
signal_cell{l} = Polarization_Controller("mode","rot_power","desired_power",100).process(Eml_out);
|
||||
end
|
||||
|
||||
Opt_sig_wdm = Optical_Multiplex("fs_in",fdac*kover,"fs_out",upsample_pow*fdac*kover,...
|
||||
@@ -119,12 +119,12 @@ for realiz = 1:num_realiz
|
||||
zdw = 1310;
|
||||
D_local = 0; %if ~=0, simulation uses "segmented fiber with d+,d-)
|
||||
randomize_D = true;
|
||||
Dvec = getDispersionVector(nSegments, D_local, zdw, randomize_D, random_key+realiz);
|
||||
for s = 1:nSegments
|
||||
Dvec = getDispersionVector(nSegments, D_local, zdw, randomize_D, s.random_key+realiz);
|
||||
for seg = 1:nSegments
|
||||
|
||||
Opt_sig_wdm_fib = DP_Fiber("L",segment_length,"D",Dvec(s),"Dpmd",pmd,"Ds",0.07,...
|
||||
"beat_len",10,"corr_len",100,"dz",1,"manakov",0,...
|
||||
"gamma",gamma,"lambda",zdw,"n_waveplates",10,"SS_dphimax",0.01,...
|
||||
"gamma",s.gamma,"lambda",zdw,"n_waveplates",10,"SS_dphimax",0.01,...
|
||||
"SS_dzmax",50,"SS_dzmin",10,"X_alpha",0.3,"X_beta",0,"rng",1).process(Opt_sig_wdm_fib);
|
||||
|
||||
end
|
||||
@@ -133,12 +133,12 @@ for realiz = 1:num_realiz
|
||||
|
||||
% Opt_sig_wdm_fib.move_it_spectrum("fignum",100212,"displayname",'bla');
|
||||
|
||||
% 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",s.link_length/1000,"alpha",0.3,"D",0,"lambda0",1310,"s.gamma",0,"Dslope",0.07).process(Opt_sig)
|
||||
|
||||
parfor ri = 1:length(rop)
|
||||
for ri = 1:length(s.rop)
|
||||
|
||||
%%%%%% ROP %%%%%%
|
||||
Opt_sig_wdm_rx = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power","amplification_db",rop(ri)+10*log10(N)).process(Opt_sig_wdm_fib);
|
||||
Opt_sig_wdm_rx = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power","amplification_db",s.rop(ri)+10*log10(N)).process(Opt_sig_wdm_fib);
|
||||
|
||||
Opt_sig_wdm_demux = Optical_Demultiplex("attenuation",0,"B",200e9,"filtype",1,"fs_out",Opt_sig_wdm_rx.fs/upsample_pow,"fs_in",Opt_sig_wdm_rx.fs,"lambda_center",1310).process(Opt_sig_wdm_rx);
|
||||
|
||||
@@ -147,9 +147,9 @@ for realiz = 1:num_realiz
|
||||
|
||||
%%%%%% PD Square Law %%%%%%
|
||||
assert(fdac*kover==Opt_sig_wdm_demux{l}.fs,'Sampling Frequencies do not match! Check previous steps');
|
||||
PD_sig = Photodiode("fsimu",fdac*kover,"dark_current",2e-08,"responsivity",1,"temperature",20,"nep",1.8e-11,"randomkey",random_key+l+realiz).process(Opt_sig_wdm_demux{l});
|
||||
PD_sig = Photodiode("fsimu",fdac*kover,"dark_current",2e-08,"responsivity",1,"temperature",20,"nep",1.8e-11,"randomkey",s.random_key+l+realiz).process(Opt_sig_wdm_demux{l});
|
||||
|
||||
PD_sig.spectrum("fignum",222,"displayname",'bla','normalizeTo0dB',1);
|
||||
% PD_sig.spectrum("fignum",222,"displayname",'bla','normalizeTo0dB',1);
|
||||
|
||||
%%%%%% Low-pass RX (PD, El. Connectors and Scope %%%%%%
|
||||
rx_bwl = 100e9;
|
||||
@@ -176,7 +176,7 @@ for realiz = 1:num_realiz
|
||||
% 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{l},Tx_bits{l},...
|
||||
ffe_results = ffe(eq_ffe,s.M,Rx_sig,Symbols{l},Tx_bits{l},...
|
||||
"precode_mode",duob_mode,...
|
||||
'showAnalysis',0,...
|
||||
"postFFE",[],...
|
||||
@@ -194,12 +194,12 @@ for realiz = 1:num_realiz
|
||||
|
||||
useviterbi = 0;
|
||||
if useviterbi
|
||||
mlse_ = MLSE_viterbi("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
|
||||
mlse_ = MLSE_viterbi("duobinary_output",0,'M',s.M,'trellis_states',PAMmapper(s.M,0).levels);
|
||||
else
|
||||
mlse_ = MLSE("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
|
||||
mlse_ = MLSE("duobinary_output",0,'M',s.M,'trellis_states',PAMmapper(s.M,0).levels);
|
||||
end
|
||||
|
||||
[vnle_results, mlse_results] = vnle_postfilter_mlse(eq_, pf_, mlse_, M, Rx_sig, Symbols{l},Tx_bits{l}, ...
|
||||
[vnle_results, mlse_results] = vnle_postfilter_mlse(eq_, pf_, mlse_, s.M, Rx_sig, Symbols{l},Tx_bits{l}, ...
|
||||
"precode_mode", duob_mode,...
|
||||
'showAnalysis', 0, ...
|
||||
"postFFE", [],...
|
||||
@@ -209,18 +209,17 @@ for realiz = 1:num_realiz
|
||||
output_mlse{l,ri,realiz} = mlse_results;
|
||||
|
||||
|
||||
|
||||
% DB tgt.
|
||||
useviterbi = 0;
|
||||
if useviterbi
|
||||
mlse_db_ = MLSE_viterbi("duobinary_output",0,'M',M,'trellis_states',PAMmapper(M,0).levels);
|
||||
mlse_db_ = MLSE_viterbi("duobinary_output",0,'M',s.M,'trellis_states',PAMmapper(s.M,0).levels);
|
||||
else
|
||||
mlse_db_ = MLSE("DIR",[1,1],"duobinary_output",0,"M",M,"trellis_states",PAMmapper(M,0).levels);
|
||||
mlse_db_ = MLSE("DIR",[1,1],"duobinary_output",0,"M",s.M,"trellis_states",PAMmapper(s.M,0).levels);
|
||||
end
|
||||
ffe_order = [50, 5, 5];
|
||||
eq_ = EQ("Ne",ffe_order,"Nb",dfe_order,"training_length",len_tr,"training_loops",5,"dd_loops",5,"K",2,"DCmu",mu_dc,"DDmu",[mu_ffe mu_dfe],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
|
||||
|
||||
dbt_results = duobinary_target(eq_, mlse_db_, M, Rx_sig, Symbols{l},Tx_bits{l}, ...
|
||||
dbt_results = duobinary_target(eq_, mlse_db_, s.M, Rx_sig, Symbols{l},Tx_bits{l}, ...
|
||||
"precode_mode", duob_mode, ...
|
||||
'showAnalysis', 0,...
|
||||
"postFFE", []);
|
||||
@@ -232,6 +231,7 @@ for realiz = 1:num_realiz
|
||||
end
|
||||
|
||||
res = struct();
|
||||
res.settings = s;
|
||||
res.ffe = output_ffe;
|
||||
res.vnle = output_vnle;
|
||||
res.mlse = output_mlse;
|
||||
@@ -240,38 +240,13 @@ for realiz = 1:num_realiz
|
||||
% Save results
|
||||
save(fullfile(output_root, fname), 'res', '-v7.3');
|
||||
fprintf('Saved results to: %s\n', fullfile(output_root, fname));
|
||||
disp(datetime('now','TimeZone','local','Format','yyyyMs.Mdd_HHmmss'));
|
||||
|
||||
|
||||
end
|
||||
|
||||
|
||||
|
||||
figure();hold on;
|
||||
cols = linspecer(N);
|
||||
for l = 1:N
|
||||
% plot(rop,mean(squeeze(ber_vnle(l,:,:)),2,'omitnan'),'Marker','*','DisplayName',sprintf('Ch: %d',wavelengthplan(l)))
|
||||
% plot(rop,cellfun(@(c) c.metrics.BER, output_ffe(l,:), 'UniformOutput', true),'Marker','*','DisplayName',sprintf('Ch: %d',wavelengthplan(l)),'Color',cols(l,:),'HandleVisibility','on','LineStyle',':');
|
||||
plot(rop,cellfun(@(c) c.metrics.BER, res.vnle(l,:), 'UniformOutput', true),'Marker','x','DisplayName',sprintf('Ch: %d',wavelengthplan(l)),'Color',cols(l,:),'HandleVisibility','on','LineStyle','--')
|
||||
plot(rop,cellfun(@(c) c.metrics.BER, res.mlse(l,:), 'UniformOutput', true),'Marker','o','DisplayName',sprintf('Ch: %d',wavelengthplan(l)),'Color',cols(l,:),'HandleVisibility','on','LineStyle','-')
|
||||
end
|
||||
yline([3.8e-3,2.2e-4],'HandleVisibility','off');
|
||||
ylabel('BER');
|
||||
xlabel('ROP')
|
||||
title('BER vs. ROP');
|
||||
set(gca, 'XScale', 'linear', ...
|
||||
'YScale', 'log', ...
|
||||
'TickLabelInterpreter', 'latex', ...
|
||||
'FontSize', 11);
|
||||
xlim([min(rop) max(rop)])
|
||||
ylim([1e-5 0.3])
|
||||
|
||||
% --- save as PNG ---
|
||||
outname = fullfile(output_root, 'BER_vs_ROP.png'); % saves to current folder
|
||||
print(gcf, outname, '-dpng', '-r300'); % 300 dpi
|
||||
fprintf('Saved figure to %s\n', outname);
|
||||
|
||||
|
||||
function dispersion_vector = getDispersionVector(N, D, ref_zdw, randomize_ZDW, randomkey)
|
||||
% MATLAB version of the Python generator shown above.
|
||||
% s.MATLAB version of the Python generator shown above.
|
||||
% Returns an N×1 vector (ps/(nm·km)).
|
||||
%
|
||||
% D is the nominal dispersion magnitude. For D>0 the link is segmented with
|
||||
|
||||
Reference in New Issue
Block a user