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This commit is contained in:
Silas Oettinghaus
2024-04-19 10:31:36 +02:00
parent 025498d120
commit ed17953407
30 changed files with 3261 additions and 1580 deletions
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24
Classes/00_signals/Electricalsignal.m
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@@ -11,25 +11,39 @@ classdef Electricalsignal < Signal
%ELECTRICALSIGNAL Construct an instance of this class
% Detailed explanation goes here
arguments
signal
signal
options.fs
options.logbook
end
obj = obj@Signal(signal);
fn = fieldnames(options);
for l = 1:numel(fn)
obj.(fn{l}) = options.(fn{l});
obj.(fn{l}) = options.(fn{l});
end
end
function pow = power(obj)
pow = mean(abs(obj.signal),"all") ;
pow = mean( abs(obj.signal).^2 ) ;
pow = pow2db(pow)+30; %dbm
end
function cspr = cspr(obj)
carrier_power_dbm = pow2db( abs(mean(obj.signal)).^2 )+30; % dB -> +30 -> dBm
signal_power_dbm = obj.power;
carr_lin = abs(mean(obj.signal)).^2;
sign_lin = mean(abs(obj.signal).^2);
cspr_lin = pow2db(carr_lin/sign_lin);
cspr =carrier_power_dbm-signal_power_dbm;
end

17
Classes/00_signals/Informationsignal.m
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@@ -3,15 +3,26 @@ classdef Informationsignal < Signal
% Detailed explanation goes here
properties
fs
end
methods
function obj = Informationsignal(signal)
function obj = Informationsignal(signal,options)
%OPTICALSIGNAL Construct an instance of this class
% Detailed explanation goes here
arguments
signal
options.fs
options.logbook
end
obj = obj@Signal(signal);
fn = fieldnames(options);
for l = 1:numel(fn)
obj.(fn{l}) = options.(fn{l});
end
end

46
Classes/00_signals/Opticalsignal.m
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@@ -32,11 +32,53 @@ classdef Opticalsignal < Signal
end
%%
function [obj,delay_n] = delay(obj,options)
arguments
obj Opticalsignal
options.delay_meter double = 0
options.delay_samples = 0
end
if options.delay_meter ~= 0
delay_t = options.delay_meter /(physconst("LightSpeed")/1.4677);
delay_n = delay_t .* obj.fs;
else
delay_n = options.delay_samples;
end
obj.signal=delayseq(obj.signal,delay_n);
end
function pow = power(obj)
pow = mean(abs(obj.signal.^2)) ;
% pow = pow2db(pow)+30;
pow = mean( abs(obj.signal).^2 ) ;
pow = pow2db(pow)+30; %dbm
end
function cspr = cspr(obj)
carrier_power_dbm = pow2db( abs(mean(obj.signal)).^2 )+30; % dB -> +30 -> dBm
signal_power_dbm = obj.power;
carr_lin = abs(mean(obj.signal)).^2;
sign_lin = mean(abs(obj.signal).^2);
cspr_lin = pow2db(carr_lin/sign_lin);
cspr =carrier_power_dbm-signal_power_dbm;
end
function papr = papr(obj)
average_power = obj.power;
peak_power = pow2db(max(abs(obj.signal.^2)))+30;
papr = peak_power - average_power;
end
end

107
Classes/00_signals/Signal.m
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@@ -12,6 +12,7 @@ classdef Signal
%SIGNAL Construct an instance of this class
% Detailed explanation goes here
obj.signal = signal;
obj.signal = obj.signal;
SignalType = [];
TimeStamp = [];
@@ -26,13 +27,18 @@ classdef Signal
end
%% CONVERT TO INFORMATIONSIGNAL
function [i_sig, varargout] = Informationsignal(obj)
function [i_sig, varargout] = Informationsignal(obj,options)
arguments
obj
options.fs
options.logbook
end
if isa(obj,'Electricalsignal')
%convert to optical
%convert to information
varargout{1} = obj.fs;
i_sig = Informationsignal(obj.signal);
i_sig = Informationsignal(obj.signal,"fs",options.fs,"logbook",options.logbook);
elseif isa(obj,'Opticalsignal')
@@ -109,12 +115,32 @@ classdef Signal
end
%% Add signals from one signal to another, the first object will sustain
function Sum = plus(X,Y)
Sum = X; %first input object will sustain
Sum.signal = X.signal + Y.signal;
end
function Sum = plus(X,y)
%% Display length
if isa(y,'Signal')
Sum = X;
Sum.signal = X.signal + y.signal;
elseif isnumeric(y)
Sum = X;
Sum.signal = X.signal + y;
end
end
function Product = times(X,y)
if isa(y,'Signal')
Product = X;
Product.signal = X.signal .* y.signal;
elseif isnumeric(y)
Product = X;
Product.signal = X.signal .* y;
end
end
%% Display length
function return_length = length(obj)
%METHOD1 Summary of this method goes here
% Detailed explanation goes here
@@ -142,7 +168,7 @@ classdef Signal
end
%% Resample Signal
%% Resample Signal
function obj = resample(obj,options)
arguments
@@ -157,6 +183,8 @@ classdef Signal
obj = obj.logbookentry(desc);
obj.fs = options.fs_out;
end
%%
@@ -262,6 +290,7 @@ classdef Signal
end
%%
function obj = normalize(obj,options)
@@ -270,7 +299,7 @@ classdef Signal
options.mode normalization_mode = normalization_mode.rms
end
switch options.mode
switch options.mode
case normalization_mode.rms
obj.signal = obj.signal/sqrt(mean(abs(obj.signal).^2,"all"));
case normalization_mode.oneone
@@ -283,20 +312,58 @@ classdef Signal
function [obj,delay_n] = delay(obj,options)
arguments
obj Opticalsignal
options.delay_meter double = 0
obj Signal
options.delay_samples = 0
end
delay_t = options.delay_meter /(physconst("LightSpeed")/1.4677);
delay_n = round(delay_t .* obj.fs);
% finally circshift the signal
% obj.signal=circshift(obj.signal,delay_n);
obj.signal=[zeros(delay_n,1); obj.signal(1:end-delay_n) ];
obj.signal=delayseq(obj.signal,options.delay_samples);
end
%%
function [obj,D,cuts] = tsynch(obj,options)
% time sync and cut
arguments
obj Signal
options.reference Signal
options.fs_ref = 0;
end
%normalize the signal
a = obj.normalize("mode","oneone").signal;
%resample the reference
q = obj.fs/options.fs_ref;
b = options.reference.resample("fs_in",options.fs_ref,"fs_out",obj.fs).normalize("mode","oneone").signal;
%estimate delay between signals
[co,lags] = xcorr(a,b,100);
[~,pos] = max(co);
D = lags(pos);
if D == 100
warning('Check Sync: Delay is found to be 100 which is the max. windowlength is xcorr function!')
end
% delay by lagging samples
obj = obj.delay("delay_samples",-D);
cuts = obj.length-(options.reference.length*q);
if cuts > 10
warning('Check Sync: Signal difference larger than 10.')
end
if cuts < 0
% warning('Check Sync: Reference Signal shorter than signal to sync.')
else
% then cut out the length of the reference
obj.signal = obj.signal(1:end-cuts);
end
end
end
end

33
Classes/01_transmit/AWG.m
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@@ -4,7 +4,6 @@ classdef AWG
properties(Access=public)
preset
kover %oversampling factor e.g. 16
repetitions %repeat the signal to generate a longer sequence?
fdac %needed
@@ -32,7 +31,6 @@ classdef AWG
% Detailed explanation goes here
arguments
options.preset = 'none';
options.kover = 16;
options.repetitions = 1;
options.normalize = 1;
@@ -45,6 +43,7 @@ classdef AWG
options.lpf_active = 0;
options.lpf_type = 0;
options.f_cutoff = 32e9;
options.H_lpf Filter
end
fn = fieldnames(options);
@@ -54,40 +53,34 @@ classdef AWG
end
end
if options.preset == "M8196A"
% M8196A (92GBd) https://www.keysight.com/us/en/product/M8196A/92-gsa-s-arbitrary-waveform-generators.html
obj.dac_max = 0.5;
obj.dac_min = -.5;
obj.f_cutoff = 50e9;
elseif options.preset == "M8199B"
%https://www.keysight.com/us/en/assets/3120-1465/data-sheets/M8199A-128-256-GSa-s-Arbitrary-Waveform-Generator.pdf
% obj.fdac = 256e9;
obj.dac_max = 0.5;
obj.dac_min = -.5;
obj.f_cutoff = 80e9;
end
end
function signalclass_out = process(obj,signalclass_in)
len_in = length(signalclass_in.signal);
% actual processing of the signal (steps 1. - 3.)
% 1-3. actual processing of the signal (normalize->quantize->sample hold)
signalclass_in.signal = obj.process_(signalclass_in.signal);
% 4. Apply LPF on the signal
if obj.lpf_active
lpf = Filter('filtdegree',5,"f_cutoff",obj.f_cutoff,"fsamp",obj.kover*obj.fdac,"filterType",obj.lpf_type);
signalclass_in = lpf.process(signalclass_in);
if isa(obj.H_lpf,'Filter')
%4.A) user already specified a complete filter class when
% initializing the AWG module
signalclass_in = obj.H_lpf.process(signalclass_in);
else
%4.B) just use a standard filter
lpf = Filter('filtdegree',5,"f_cutoff",obj.f_cutoff,"fsamp",obj.kover*obj.fdac,"filterType",obj.lpf_type);
signalclass_in = lpf.process(signalclass_in);
end
end
% cast the inform. signal to electrical signal
signalclass_in = Electricalsignal(signalclass_in,"fs",obj.fdac*obj.kover,"logbook",signalclass_in.logbook);
% append to logbook
lbdesc = ['AWG preset ', obj.preset, 'k_over:',num2str(obj.kover),'. f_dac:',num2str(obj.fdac*1e-9),'GHz. Resolution:',num2str(obj.bit_resolution),' bits.'];
current_class = class(obj);
lbdesc = ['AWG ', current_class , '// k_over:',num2str(obj.kover),'. f_dac:',num2str(obj.fdac*1e-9),'GHz. Resolution:',num2str(obj.bit_resolution),' bits.'];
signalclass_in = signalclass_in.logbookentry(lbdesc);
% write to output

35
Classes/01_transmit/M8196A.m Normal file
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@@ -0,0 +1,35 @@
classdef M8196A < AWG
properties
end
methods
function obj = M8196A()
%This is a ready to use AWG simulation that resembles the
%properties of the Keysighe M8196A % M8196A (92GBd) https://www.keysight.com/us/en/product/M8196A/92-gsa-s-arbitrary-waveform-generators.html
arguments
%options.bla = 1;
end
obj = obj@AWG();
obj.dac_max = 0.5;
obj.dac_min = -.5;
obj.bit_resolution = 5.5;
obj.fdac = 92e9;
obj.lpf_active = 1;
obj.f_cutoff = 32e9;
obj.lpf_type = filtertypes.gaussian;
obj.H_lpf = Filter('filtdegree',5,"f_cutoff",obj.f_cutoff,"fsamp",obj.kover*obj.fdac,"filterType",obj.lpf_type);
end
end
end

34
Classes/01_transmit/M8199B.m Normal file
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@@ -0,0 +1,34 @@
classdef M8199B < AWG
properties
end
methods
function obj = M8199B()
%This is a ready to use AWG simulation that resembles the
%properties of the Keysighe M8199B https://www.keysight.com/us/en/assets/3120-1465/data-sheets/M8199A-128-256-GSa-s-Arbitrary-Waveform-Generator.pdf
arguments
%options.bla = 1;
end
obj = obj@AWG();
obj.dac_max = 0.5;
obj.dac_min = -.5;
obj.bit_resolution = 5.5;
obj.fdac = 256e9;
obj.lpf_active = 1;
obj.f_cutoff = 80e9;
obj.lpf_type = filtertypes.gaussian;
obj.H_lpf = Filter('filtdegree',5,"f_cutoff",obj.f_cutoff,"fsamp",obj.kover*obj.fdac,"filterType",obj.lpf_type);
end
end
end

51
Classes/01_transmit/PAMmapper.m
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@@ -19,10 +19,16 @@ classdef PAMmapper
end
function signalclass_out = map(obj,signalclass_in)
signalclass_in.signal = obj.map_(signalclass_in.signal);
signalclass_in = signalclass_in.logbookentry();
signalclass_out = signalclass_in;
function out = map(obj,signal_in)
if isa(signal_in,'Signal')
signal_in.signal = obj.map_(signal_in.signal);
signal_in = signal_in.logbookentry();
out = signal_in;
else
out = signal_in;
end
end
function signalclass_out = demap(obj,signalclass_in)
@@ -157,7 +163,7 @@ classdef PAMmapper
case 2
% 4-ASK
data_out=[comp_real(:,:,2); ones(s1,s2)-comp_real(:,:,1)+comp_real(:,:,3)];
data_out=[comp_real(:,:,2); ones(s1,s2) - comp_real(:,:,1) + comp_real(:,:,3)];
case 3
@@ -179,16 +185,43 @@ classdef PAMmapper
end
function [data_out] = decide_pamlevel(obj,data_in)
function [data_out] = decide_pamlevel(obj,data_in,options)
arguments
obj
data_in Signal
options.symbol_levels = []
end
%A) normally return the preproduct of the decision
a = squeeze(repmat(real(data_in.signal),[1 1 length(obj.thresholds)])); %Eingangssignal in 3 spalten
b = squeeze(repmat(reshape(obj.thresholds(:).',[1 1 length(obj.thresholds)]),[1 length(data_in.signal) 1])); %Threshold in 3 Spalten
comp_real = a > b; %check for each symbol/ sampling if it exeeds the obj.thresholdseshold 1, 2 or 3
data_out = data_in;
data_out.signal = sum(comp_real,2);
%Option: return the actual level values/ just map onto given
%symbol levels
if ~isempty(options.symbol_levels)
data_out.signal = options.symbol_levels(data_out.signal+1);
end
end
data_out = sum(comp_real,2);
function [out] = separate_pamlevels(obj,data_in)
%A) normally return the preproduct of the decision
a = squeeze(repmat(real(data_in.signal),[1 1 length(obj.thresholds)])); %Eingangssignal in 3 spalten
b = squeeze(repmat(reshape(obj.thresholds(:).',[1 1 length(obj.thresholds)]),[1 length(data_in.signal) 1])); %Threshold in 3 Spalten
comp_real = a > b; %check for each symbol/ sampling if it exeeds the obj.thresholdseshold 1, 2 or 3
comp_real_sum = sum(comp_real,2);
%data_out = (data_out*2)-3;
out = NaN(length(data_in),length(obj.thresholds)+1);
for idx = 1:length(data_in)
out(idx,comp_real_sum(idx)+1) = data_in.signal(idx);
end
%data_out = pam_level_decision .* 1/sqrt(5);
end
end

20
Classes/01_transmit/Pulseformer.m
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@@ -5,7 +5,7 @@ classdef Pulseformer
properties(Access=public)
fdac
fsym
pulseform
pulse
pulselength
rrcalpha
end
@@ -18,7 +18,7 @@ classdef Pulseformer
arguments
options.fdac double
options.fsym double
options.pulseform pulseform = pulseform.rrc
options.pulse pulseform = pulseform.rrc
options.pulselength double {mustBeInteger} = 32
options.rrcalpha double = 0.05
end
@@ -44,6 +44,9 @@ classdef Pulseformer
lbdesc = 'Applied Pulseshaping';
signalclass_in = signalclass_in.logbookentry(lbdesc);
% write fs to signal
signalclass_in.fs = obj.fdac;
% write to output
signalclass_out = signalclass_in;
@@ -62,11 +65,11 @@ classdef Pulseformer
% Detailed explanation goes here
arguments(Input)
obj
data_in double
data_in
end
arguments(Output)
data_out double
data_out
end
if ~rem(obj.fdac,obj.fsym)
@@ -81,9 +84,9 @@ classdef Pulseformer
sps= up;
end
if obj.pulseform == pulseform.rrc
if obj.pulse == pulseform.rrc
%Bau das Filter (hier rrc)
racos_len = obj.pulselength*2 ;
racos_len = obj.pulselength*2;
alpha = obj.rrcalpha;
h = rcosdesign(alpha,racos_len,sps);
h = h./ max(h);
@@ -115,12 +118,13 @@ classdef Pulseformer
% %Apply Filter using Matlab build in fctn.
% data_out = upfirdn(data_in,h,up,dn);
h = rcosdesign(alpha,racos_len,sps);
h = h./ max(h);
%data_out_ = upfirdn(data_in,h,up,dn);
%
% %cut signal, which is longer due to fir filter
st = round(up/dn*racos_len/2); %we need to cut y_out
% en = round(st + (length(data_in)*up/dn) -1);
%
% data_out = data_out(st:en);
%scaling?! see pulsef module line 696

195
Classes/02_etc/Filter.m
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@@ -1,4 +1,4 @@
classdef Filter
classdef Filter < handle
%FILTER Summary of this class goes here
% Detailed explanation goes here
@@ -12,7 +12,7 @@ classdef Filter
filtdegree
passband_ripple
stopband_ripple
fsamp
fs
w
end
@@ -24,7 +24,8 @@ classdef Filter
arguments
options.filterType filtertypes = filtertypes.bessel_inp ;
options.f_cutoff = 0;
options.fsamp = 0;
options.fs = 0;
options.signal_length = 0;
options.filtdegree = 3;
options.passband_ripple = 0.5;
options.stopband_ripple = 0.5;
@@ -36,7 +37,12 @@ classdef Filter
for n = 1:numel(fn)
obj.(fn{n}) = options.(fn{n});
end
% *Nur* wenn die beiden gegeben wurde, kann das Filter vorab
% erstellt werden - sonst zur Laufzeit
if obj.fs ~= 0 && obj.signal_length ~= 0
[obj.H,obj.w] = obj.buildFilter();
end
end
@@ -61,75 +67,77 @@ classdef Filter
end
function yout = process_(obj,xin)
obj.signal_length = length(xin);
[obj.H,obj.w] = obj.buildFilter(obj.filterType);
if isempty(obj.H) || obj.signal_length ~= length(obj.H)
[obj.H,obj.w] = obj.buildFilter();
end
yout = obj.applyFilter(xin);
end
end
function y_filtered = applyFilter(obj,xin)
y_filtered = ifft(obj.H.*fft(xin));
end
function [H,w] = buildFilter(obj,filterType)
function [H,w] = buildFilter(obj)
w = [];
rp = obj.passband_ripple; %passband ripple
rs = obj.stopband_ripple; %stopband ripple
switch filterType
switch obj.filterType
case 1
% Bessel filter, impulse invariant transformed
[B, A] = besself(obj.filtdegree, 2*pi*obj.f_cutoff);
[B ,A] = impinvar(B,A,obj.fsamp);
[B ,A] = impinvar(B,A,obj.fs);
case 2
% Bessel filter, impulse bilinear transformed
[Z, P, K] = besself(obj.filtdegree, 2*pi*obj.f_cutoff);
[Z ,P, K] = bilinear(Z,P,K,obj.fsamp);
[Z ,P, K] = bilinear(Z,P,K,obj.fs);
[B ,A] = zp2tf(Z ,P ,K);
case 3
% Butterworth filter
if obj.lowpass == 1 %lowpass
[B, A] = butter(obj.filtdegree, obj.f_cutoff/(obj.fsamp/2),'low');
[B, A] = butter(obj.filtdegree, obj.f_cutoff/(obj.fs/2),'low');
else % highpass
[B, A] = butter(obj.filtdegree, obj.f_cutoff/(obj.fsamp/2),'high');
[B, A] = butter(obj.filtdegree, obj.f_cutoff/(obj.fs/2),'high');
end
case 4
% Chebyshev 1 filter
[B, A] = cheby1(obj.filtdegree,rp, obj.f_cutoff/(obj.fsamp/2));
[B, A] = cheby1(obj.filtdegree,rp, obj.f_cutoff/(obj.fs/2));
case 5
% Chebyshev 2 filter
[B, A] = cheby2(obj.filtdegree,rs, obj.f_cutoff/(obj.fsamp/2));
[B, A] = cheby2(obj.filtdegree,rs, obj.f_cutoff/(obj.fs/2));
case 6
% Elliptic filter
[B, A] = ellip(obj.filtdegree,rp,rs,obj.f_cutoff/(obj.fsamp/2));
[B, A] = ellip(obj.filtdegree,rp,rs,obj.f_cutoff/(obj.fs/2));
case 7
% Hamming filter
g=(obj.filtdegree-1)/2;
wc=obj.f_cutoff/(obj.fsamp/2);
wc=obj.f_cutoff/(obj.fs/2);
B = wc*sinc(wc*(-g:g)).*hamming(obj.filtdegree)';
A=1;
@@ -137,7 +145,7 @@ classdef Filter
% Raised Cosine filter
B = firrcos(obj.filtdegree,obj.f_cutoff,para.df,obj.fsamp);
B = firrcos(obj.filtdegree,obj.f_cutoff,df,obj.fs);
A=1;
case 9
@@ -145,7 +153,7 @@ classdef Filter
% Sinc filter
g=(obj.filtdegree-1)/2;
wc=obj.f_cutoff/(obj.fsamp/2);
wc=obj.f_cutoff/(obj.fs/2);
B = wc*sinc(wc*(-g:g));
A=1;
@@ -155,55 +163,122 @@ classdef Filter
%check if order ist multiple of 1/2
blocklen=obj.signal_length;
faxis=linspace(-obj.fsamp/2,obj.fsamp/2,blocklen+1)';%generates arow vector faxis of blocklen+1 points linearly spaced between and including -para.fs/2 and para.fs/2
faxis=linspace(-obj.fs/2,obj.fs/2,blocklen+1)';%generates arow vector faxis of blocklen+1 points linearly spaced between and including -fs/2 and fs/2
faxis=ifftshift(faxis(1:end-1));
H=exp(-((faxis)/(obj.f_cutoff*2)).^(2*obj.filtdegree)*log(2)*2^(2*obj.filtdegree-1));
% figure()
% hold on
% xline(obj.f_cutoff*1e-9,'LineWidth',3,LineStyle='--');
% xline(-obj.f_cutoff*1e-9,'LineWidth',3,LineStyle='--');
% plot(faxis*1e-9,20*log10(abs(H)),'LineWidth',3);
% ax = gca;
% ylim([-6 0])
% grid on
% xlabel('Freq in GHz')
% ylabel('Magnitude (dB)')
end
% Build Filter from coefficients
if filterType ~= 10
if obj.filterType ~= 10
[H,w] = freqz(B, A, obj.signal_length,'whole');
% figure()
% hold on
% plot(w/(2*pi)*obj.fsamp*1e-9,20*log10(abs(H)),'LineWidth',3);
% ax = gca;
% %ylim([-6 0])
% grid on
% xlabel('Freq in GHz')
% ylabel('Magnitude (dB)')
% figure(30)
% freqz(B, A, 2048, obj.fsamp);
% hfvt = fvtool(B,A);
end
end
function show(obj)
obj.signal_length = 1024;
[H,w] = obj.buildFilter(obj.filterType);
plot(w/pi,20*log10(abs(H)));
ax = gca;
ax.XTick = 0:.5:2;
grid on
xlabel('Normalized Frequency (\times\pi rad/sample)')
ylabel('Magnitude (dB)')
%freqz(H);
obj.signal_length = 512;
[H_,~] = obj.buildFilter();
figure()
sgtitle('- Filter Visualization -','FontSize',11)
hold on
fs = obj.fs;
frex = linspace(-fs/2,fs/2,numel(H_)).*1e-9;
filt = fftshift(20*log10(abs(H_)));
filt_angle = fftshift(angle(H_));
filt_grpdly = diff(unwrap(filt_angle))./diff(frex.');
fc = obj.f_cutoff;
fcut_= [-fc*1e-9;fc*1e-9];
[~, index] = min(abs(filt - (-3)));
threedB = frex(index);
cur_module_name = '';
filter_desc = char([num2str(obj.filtdegree),'th-order ',char(obj.filterType),'; f cut:',num2str(fc.*1e-9),' GHz']);
legend Interpreter none
subplot(3,1,1);
legend Interpreter none
title('Magnitude')
hold on
p = plot(frex,filt,'LineWidth',1,'LineStyle','-','DisplayName',[cur_module_name,': ',filter_desc]);
xline([-threedB,threedB],'LineStyle','-','LineWidth',1,'HandleVisibility','off','Color',p.Color);
xline(fcut_,'LineStyle',':','LineWidth',1,'HandleVisibility','off','Color',p.Color);
yline(-3,'LineStyle',':','LineWidth',1,'HandleVisibility','off');
ylim([-10 0])
xlim([-200 200])
ylabel('Magnitude (dB)')
legend
subplot(3,1,2);
legend Interpreter none
title('Phase')
hold on
p = plot(frex,filt_angle,'LineWidth',1,'Color',p.Color,'LineStyle',':','DisplayName',[cur_module_name,': ',filter_desc]);
ylim([-pi,pi]);
yticks([-pi, 0, pi]);
yticklabels({'-$\pi$', '0', '$\pi$'});
ylabel('Angle (rad)');
subplot(3,1,3);
legend Interpreter none
title('Group Delay')
hold on
p = plot(frex(2:end),filt_grpdly,'LineWidth',1,'Color',p.Color,'LineStyle',':','DisplayName',[cur_module_name,': ',filter_desc]);
ylabel('Angle (rad)');
ax = gca;
xlim([-200 200])
grid on
xlabel('Freq in GHz')
legend
end
function showHere(obj)
obj.signal_length = 512;
[H_,~] = obj.buildFilter();
fs = obj.fs;
frex = linspace(-fs/2,fs/2,numel(H_)).*1e-9;
filt = fftshift(20*log10(abs(H_)));
filt_angle = fftshift(angle(H_));
filt_grpdly = diff(unwrap(filt_angle))./diff(frex.');
fc = obj.f_cutoff;
fcut_= [-fc*1e-9;fc*1e-9];
[~, index] = min(abs(filt - (-3)));
threedB = frex(index);
cur_module_name = '';
filter_desc = char([num2str(obj.filtdegree),'th-order ',char(obj.filterType),'; f cut:',num2str(fc.*1e-9),' GHz']);
legend Interpreter none
legend Interpreter none
title('Magnitude')
hold on
p = plot(frex,filt,'LineWidth',1,'LineStyle','-','DisplayName',[cur_module_name,': ',filter_desc]);
xline([-threedB,threedB],'LineStyle','-','LineWidth',1,'HandleVisibility','off','Color',p.Color);
xline(fcut_,'LineStyle',':','LineWidth',1,'HandleVisibility','off','Color',p.Color);
yline(-3,'LineStyle',':','LineWidth',1,'HandleVisibility','off');
legend
end
end
end

9
Classes/02_optical/EML.m
View File

@@ -14,6 +14,7 @@ classdef EML
bias
u_pi
randomkey
randomstream
%on instance creation
field
@@ -49,6 +50,8 @@ classdef EML
obj.(fn{l}) = options.(fn{l});
end
obj.randomstream = RandStream('mlfg6331_64','Seed',obj.randomkey);
obj.field=sqrt(10^(obj.power/10-3)); %dbm to sqrt(mw)
obj.noisefactor=sqrt(2*pi*obj.linewidth/obj.fsimu);
@@ -105,8 +108,9 @@ classdef EML
end
function noi = createPhaseNoise(obj)
%create random vector
noi = randn(obj.signal_len/2,1);
noi = randn(obj.randomstream,obj.signal_len,1);
%scale with noisefactor
noi = noi * obj.noisefactor;
@@ -114,9 +118,6 @@ classdef EML
%cumsum to accumulate noise over time vector
noi = cumsum(noi);
noi = [noi ; flip(noi)];
end
function modulated_laserfield = externalmodulation(obj,laserfield,electrical_in)

2
Classes/02_optical/Fiber.m
View File

@@ -29,7 +29,7 @@ classdef Fiber
options.D = 17
options.Dslope = 0.06
options.lambda0 = 1550
options.gamma = 0.0013
options.gamma = 0
options.dphimax = 5e-3
end

28
Classes/03_receive/Photodiode.m
View File

@@ -8,6 +8,9 @@ classdef Photodiode
dark_current
temperature
randomkey
randomstream
end
methods
@@ -18,14 +21,16 @@ classdef Photodiode
options.fsimu
options.responsivity = 1;
options.dark_current = 0;
options.temperature = 20;
options.temperature = 20;
options.randomkey = 1;
end
obj.fsimu = options.fsimu;
obj.responsivity = options.responsivity;
obj.dark_current = options.dark_current;
obj.temperature = options.temperature;
fn = fieldnames(options);
for l = 1:numel(fn)
obj.(fn{l}) = options.(fn{l});
end
obj.randomstream = RandStream('mlfg6331_64','Seed',obj.randomkey);
end
function signalclass_out = process(obj,signalclass_in)
@@ -50,15 +55,16 @@ classdef Photodiode
% Detailed explanation goes here
k = Constant.Boltzmann;
q = Constant.ElementaryCharge;
T = obj.temperature + 273.15 ; %celsius + 273 = kelvin
R = 50; %resistance of phdiode (50ohm is typical value)
% Magnitude squared detection
yout = sum( abs(xin) .^2*obj.responsivity, 2 ) ;
% Magnitude squared detection (sum over pols)
yout = sum( abs(xin) .^2 * obj.responsivity, 2 ) ;
% Shot Noise
shot_noise = sqrt(k * obj.fsimu .* yout) .* randn(size(yout,1),1);
shot_noise = sqrt(q * obj.fsimu .* yout) .* randn(obj.randomstream,size(yout,1),1);
yout = yout + shot_noise;
@@ -68,7 +74,7 @@ classdef Photodiode
Bw = obj.fsimu; %is this correct? shouldnt it be the bandwidth of the actual component? e.g. 70GHz?
therm_noise_pow = therm_current_psd * 2 * Bw; %squared
therm_noise = sqrt(therm_noise_pow) .* randn(size(yout,1),1);
therm_noise = sqrt(therm_noise_pow) .* randn(obj.randomstream,size(yout,1),1);
yout = yout + therm_noise;

27
Classes/03_receive/Scope.m
View File

@@ -17,8 +17,10 @@ classdef Scope
fixed_delay %fix the delay of the filter or use minimal delay for kausal system
delay %specify a fixed delay of the filter
lpf_active
filtertype
lpf_bw
H_lpf
block_dc
@@ -47,8 +49,10 @@ classdef Scope
options.fixed_delay = 0;
options.delay = 0;
options.lpf_active = 0;
options.filtertype = filtertypes.bessel_bilin;
options.lpf_bw = 120e9;
options.H_lpf;
options.block_dc = 1;
end
@@ -65,12 +69,27 @@ classdef Scope
function signalclass_out = process(obj,signalclass_in)
% apply LPF
lpf = Filter('filtdegree',3,"f_cutoff",obj.lpf_bw,"fsamp",obj.fsimu,"filterType",obj.filtertype);
signalclass_in = lpf.process(signalclass_in);
% actual processing of the signal
signalclass_in.signal = obj.process_(signalclass_in.signal);
signalclass_in.fs = obj.fadc;
% apply LPF
% 4. Apply LPF on the signal
assert (signalclass_in.fs == obj.H_lpf.fs)
if obj.lpf_active
if isa(obj.H_lpf,'Filter')
%4.A) user already specified a complete filter class when
% initializing the AWG module
signalclass_in = obj.H_lpf.process(signalclass_in);
else
%4.B) just use a standard filter
obj.H_lpf = Filter('filtdegree',3,"f_cutoff",obj.lpf_bw,"fsamp",obj.fsimu,"filterType",obj.filtertype);
signalclass_in = obj.H_lpf.process(signalclass_in);
end
end
% cast the electrical signal to information signal
signalclass_in = Informationsignal(signalclass_in,"fs",obj.fadc,"logbook",signalclass_in.logbook);
% append to logbook
lbdesc = ['Scope '];

9
Classes/04_DSP/EQ.m
View File

@@ -52,6 +52,8 @@ classdef EQ
coeff_number
constellation_in
error_log
end
methods
@@ -258,6 +260,7 @@ classdef EQ
e_dfe = b_.'*reference_vec;
error = e_dc + e_ffe - e_dfe - ref_in(m-obj.k0);
%error = e_dc + e_.'*input_vec - b_.'*reference_vec - ref_in(m-obj.k0);
if real(obj.FFEmu)
@@ -279,7 +282,11 @@ classdef EQ
end
e_dc = e_dc - obj.DCmu*error;
error_log(cnt,trainloops) = e_dc;
obj.error_log.e_ffe(cnt,trainloops) = e_ffe;
obj.error_log.e_dfe(cnt,trainloops) = e_dfe;
obj.error_log.e_(cnt,trainloops) = error;
cnt = cnt+1;
if obj.Nb(1) > 0
b_ = b_ + obj.DFEmu*error*reference_vec; % Seems like normalized DFE has worse performance

95
Classes/04_DSP/EQ_silas.m
View File

@@ -51,9 +51,9 @@ classdef EQ_silas < handle
trainloops
ddloops
eq_blocklength % block lengt of EQ (until now, only the dc subtraction is affected by this)
eq_parallelization_blocklength % block lengt of EQ (until now, only the dc subtraction is affected by this)
eq_updatelatency % time in symbols until the calculated updates reach the signal again (until now, only the dc subtraction is affected by this)
eq_avg_blocklength
end
@@ -79,8 +79,9 @@ classdef EQ_silas < handle
options.mu_ffe_dd = [0.0004 0.0005 0.0006];
options.mu_dfe_dd = 0.0005;
options.eq_blocklength = 1;
options.eq_parallelization_blocklength = 1;
options.eq_updatelatency = 1;
options.eq_avg_blocklength = 0;
end
fn = fieldnames(options);
@@ -151,8 +152,8 @@ classdef EQ_silas < handle
%% Adaptive Equalization Modes
function trainingMode(obj)
dc_block = ones(obj.eq_blocklength,1);
dc_block = ones(obj.eq_parallelization_blocklength,1);
for tloop = 1:obj.trainloops
m = 1+obj.delay;
@@ -176,7 +177,7 @@ classdef EQ_silas < handle
% Calculate the Error
obj.error = obj.e_dc + obj.e_ffe - obj.e_dfe - obj.d(obj.Nb(1)-1+m-obj.delay);
if obj.mu_ffe_train ~= 0
%update FFE coefficients with LMS
@@ -191,8 +192,8 @@ classdef EQ_silas < handle
%update DC error
dc_block(dc_cnt) = obj.error .* obj.mu_dc_train;
if dc_cnt == obj.eq_blocklength
if dc_cnt == obj.eq_parallelization_blocklength
obj.e_dc = obj.e_dc - mean(dc_block(dc_cnt));
dc_cnt = 0;
end
@@ -208,8 +209,10 @@ classdef EQ_silas < handle
%start the dd mode with coefficients from training
coeff = [obj.e;obj.b];
obj.e_dc = ones(obj.eq_updatelatency,1).*obj.e_dc;
dc_block = ones(obj.eq_parallelization_blocklength,1);
dc_block = ones(obj.eq_blocklength,1);
for ddloop = 1:obj.ddloops
@@ -227,50 +230,96 @@ classdef EQ_silas < handle
mu_dfe = ones(1,sum(obj.Cb))*obj.mu_dfe_dd;
y = zeros(1,floor(obj.x_length/obj.sps));
d_feedback = zeros(obj.Cb(1),1);
d_vnle = obj.calcVNLENonlinVecs(d_feedback,obj.Ib2,obj.Ib3,obj.Nb,obj.d_norm);
d_hat = zeros(obj.x_length,1);
m_reg = 0;
if obj.eq_avg_blocklength > 0
averaging_window = zeros(obj.eq_avg_blocklength,1);
end
for k = 1:obj.sps:obj.x_length
dc_cnt = dc_cnt+1;
m=m+1;
%get Sigal input vectors with correct length for VNLE
x = obj.x_in(obj.Ne(1)+k-1:-1:k).';
%
if obj.eq_avg_blocklength > 0 %% Das läuft gut mit 400er Fenster!!
averaging_window = circshift(averaging_window,obj.sps);
averaging_window(1:obj.sps,1) = x(1:obj.sps);
avg_(k) = mean(averaging_window);
x = x-avg_(k);
end
%bring this signal to "special" VNLE format
x_vnle = obj.calcVNLENonlinVecs(x,obj.Ie2,obj.Ie3,obj.Ne,obj.x_norm);
%combine FFE with DFE to one vector (cursor between the two sequences)
x_d = [x_vnle;-d_vnle];
%Apply filter
y(m) = obj.e_dc + x_d.'* coeff;
%y(m) = (m_reg(end)*dc_cnt + obj.e_dc(end)) + x_d.'* coeff;
if obj.mu_dc_dd > 0
y(m) = obj.e_dc(end) + x_d.'* coeff;
else
y(m) = x_d.'* coeff;
end
% if obj.eq_avg_blocklength > 0 %% Das läuft nicht gut!!
% averaging_window = circshift(averaging_window,obj.sps);
% averaging_window(1:obj.sps,1) = y(m);
% avg_(m) = mean(averaging_window);
% y(m) = y(m)-avg_(m);
% end
%Decision
[~,symbol_idx] = min(abs(y(m) - obj.d_constellation)); % decision for closest constellation point
d_hat(k) = obj.d_constellation(symbol_idx);
%Error between FFE & DFE filtered signal and Decision
obj.error = y(m) - d_hat(k);
obj.error(k) = y(m) - d_hat(k);
%Update coefficients (both FFE and DFE)
% obj.e = obj.e - obj.error * mu_ffe * conj(x_vnle);
% obj.b = obj.b + obj.error * mu_dfe * conj(d_vnle);
% coeff = [obj.e;obj.b];
% if obj.eq_avg_blocklength > 0 %% Das läuft nicht gut!!
% averaging_window = circshift(averaging_window,obj.sps);
% averaging_window(1:obj.sps,1) = y(m);
% avg_(m) = mean(averaging_window);
% y(m) = y(m)-avg_(m);
% end
coeff = coeff - mu_mat*obj.error*conj(x_d);
%update DC error
dc_block(dc_cnt) = obj.error .* obj.mu_dc_dd;
%Update FFE and DFE coefficients
coeff = coeff - mu_mat*obj.error(k) * conj(x_d);
%Update DC error
dc_block(dc_cnt) = obj.error(k) ;
if dc_cnt == obj.eq_parallelization_blocklength
if obj.eq_updatelatency > 1
obj.e_dc = circshift(obj.e_dc,1);
% m_reg(end+1) = ((1:obj.eq_parallelization_blocklength)' \ (cumsum(dc_block)));
%
% obj.e_dc(1) = obj.e_dc(2) - sign(m_reg(end)) .* (sum(dc_block).* m_reg(end) .* obj.mu_dc_dd);
obj.e_dc(1) = obj.e_dc(2) - sum(dc_block) .* obj.mu_dc_dd;
else
%m_reg(end+1) = ((1:obj.eq_parallelization_blocklength)' \ (cumsum(dc_block)));
% obj.e_dc = obj.e_dc - sign(m_reg(end)) .* (sum(dc_block).* m_reg(end) .* obj.mu_dc_dd);
obj.e_dc = obj.e_dc - sum(dc_block) .* obj.mu_dc_dd;
end
if dc_cnt == obj.eq_blocklength
obj.e_dc = obj.e_dc - mean(dc_block(dc_cnt));
dc_cnt = 0;
end
% Append new decision to decision feedback
if obj.Nb(1) > 0

764
Classes/04_DSP/EQ_silas_plain.m
View File

@@ -1,369 +1,395 @@
classdef EQ_silas_plain < handle
%EQ_SILAS FFE and DFE Equalizer Playground
properties
% Important Signals
x_in %Input Sequence to be equalized
x_length
x_norm
d %reference signal
d_norm
d_constellation %constellation points of the reference
y_out %equalizer output signal
% FFE coefficients always named with "e"
Ne
Ce %memory length FFE
Ie1 %Indice Combination of 1nd order FFE
Ie2 %Indice Combination of 2nd order FFE
Ie3 %Indice Combination of 3nd order FFE
e %coefficients for FFE
% DFE coefficients always named with "b"
Nb
Cb %memory length DFE
Ib1 %Indice Combination of 1nd order DFE
Ib2 %Indice Combination of 2nd order DFE
Ib3 %Indice Combination of 3nd order DFE
b %coefficients for DFE
error
e_ffe
e_dfe
e_dc
error_log
mu_dc_train
mu_ffe_train
mu_dfe_train
mu_dc_dd
mu_combined_dd
delay
trainlength
sps
trainloops
ddloops
end
methods
function obj = EQ_silas_plain(options)
%EQ_SILAS Construct an instance of this class
arguments(Input)
options.Ne = [50 5 0] %Number of FFE coefficients (1st, 2nd and 3rd order)
options.Nb = [30 5 3] %Number of DFE coefficients (1st, 2nd and 3rd order)
options.trainloops = 2;
options.trainlength = 4096;
options.ddloops = 2;
options.delay = 0;
options.sps = 2;
options.mu_dc_train = 0.01;
options.mu_ffe_train = 0.005;
options.mu_dfe_train = 0.005;
options.mu_dc_dd = 0.01;
options.mu_combined_dd = [0.0004 0.0005 0.0006 0.0007 ];
end
fn = fieldnames(options);
for n = 1:numel(fn)
obj.(fn{n}) = options.(fn{n});
end
% Generate helpful vectors and initialize the filters with
% correct length:
obj.Ce = obj.calcVNLEMemoryLength(obj.Ne);
[obj.Ie2,obj.Ie3] = obj.calcIndiceVectors(obj.Ne);
obj.e = zeros(sum(obj.Ce),1);
obj.Cb = obj.calcVNLEMemoryLength(obj.Nb);
[obj.Ib2,obj.Ib3] = obj.calcIndiceVectors(obj.Nb);
obj.b = zeros(sum(obj.Cb),1);
end
function [signalclass_out,error_log] = process(obj,signalclass_in, reference_signalclass_in)
% actual processing of the signal (steps 1. - 3.)
% 1 normalize RMS
signalclass_in = signalclass_in.normalize("mode","rms");
% Process the EQ optimization
obj.process_(signalclass_in.signal', reference_signalclass_in.signal');
signalclass_in.signal = obj.y_out';
% append to logbook
lbdesc = ['EQ von Silas ist gelaufen '];
signalclass_in = signalclass_in.logbookentry(lbdesc);
% write to output
signalclass_out = signalclass_in;
end
function process_(obj,x_in,d_in)
% 1) prepare signals
obj.e_dc = mean(x_in);
% 1.1) Input Signal
obj.x_in = [zeros(1,floor(obj.Ne(1)/2)) x_in zeros(1,obj.Ne(1))];
obj.x_length = length(x_in);
obj.x_norm = obj.calcPowerNormalization(x_in);
% 1.2 Reference Signal // Constellation
obj.d = [zeros(1,obj.Nb(1)-1) d_in zeros(1,obj.Nb(1))];
obj.d_constellation = unique(d_in);
obj.d_norm = obj.calcPowerNormalization(d_in);
% 1.3 Training
obj.trainingMode();
% 1.4 Decision Directed Mode
obj.decisionDirectedMode();
end
%% Adaptive Equalization Modes
function trainingMode(obj)
for tloop = 1:obj.trainloops
m = 1+obj.delay;
for n = obj.sps*obj.delay+1:obj.sps:obj.sps*obj.trainlength
m = m+1;
%get Sigal input vectors with correct length for VNLE
x_in_block = obj.x_in(obj.Ne(1)+n+(obj.sps-1):-1:n+obj.sps).';
x_in_vnle_format = obj.calcVNLENonlinVecs(x_in_block,obj.Ie2,obj.Ie3,obj.Ne,[1,1,1]);
%get Reference input vectors with correct length for VNLE
d_block = obj.d(obj.Nb(1)-obj.delay+m-2:-1:m-obj.delay-1).';
d_vnle_format = obj.calcVNLENonlinVecs(d_block,obj.Ib2,obj.Ib3,obj.Nb,obj.d_norm);
% Calculate the Error
obj.e_ffe = obj.e.' * x_in_vnle_format;
obj.e_dfe = obj.b.' * d_vnle_format;
obj.error = obj.e_dc + obj.e_ffe - obj.e_dfe - obj.d(obj.Nb(1)-1+m-obj.delay);
%update FFE coefficients with LMS
obj.e = obj.e - obj.error*conj(x_in_vnle_format)*obj.mu_ffe_train;
%update DFE coefficients with LMS
obj.b = obj.b + obj.mu_dfe_train*obj.error*d_vnle_format;
%update DC error
obj.e_dc = obj.e_dc - obj.error .* obj.mu_dc_train;
end
end
end
function decisionDirectedMode(obj)
%start the dd mode with coefficients from training
coeff = [obj.e;obj.b];
for ddloop = 1:obj.ddloops
m = 0;
if all(obj.mu_combined_dd == obj.mu_combined_dd(1))
mu_mat = obj.mu_combined_dd(1);
else
mu_mat = diag([ones(1,obj.Ce(1))*obj.mu_combined_dd(1)... %1st order ffe
ones(1,obj.Ce(2))*obj.mu_combined_dd(2)... %2nd order ffe
ones(1,obj.Ce(3))*obj.mu_combined_dd(3)... %3rd order ffe
ones(1,sum(obj.Cb))*obj.mu_combined_dd(4)]); %all order dfe
end
y = zeros(1,floor(obj.x_length/obj.sps));
d_feedback = zeros(obj.Cb(1),1);
d_vnle = obj.calcVNLENonlinVecs(d_feedback,obj.Ib2,obj.Ib3,obj.Nb,obj.d_norm);
d_hat = zeros(obj.x_length,1);
for k = 1:obj.sps:obj.x_length
m=m+1;
%get Sigal input vectors with correct length for VNLE
x = obj.x_in(obj.Ne(1)+k-1:-1:k).';
x_vnle = obj.calcVNLENonlinVecs(x,obj.Ie2,obj.Ie3,obj.Ne,[1,1,1]);
%combine FFE with DFE to one vector (cursor between the two sequences)
x_d = [x_vnle;-d_vnle];
%Apply filter
y(m) = obj.e_dc + x_d.'* coeff;
%Decision
[~,symbol_idx] = min(abs(y(m) - obj.d_constellation)); % decision for closest constellation point
d_hat(k) = obj.d_constellation(symbol_idx);
%Error between FFE & DFE filtered signal and Decision
obj.error = y(m) - d_hat(k);
%Update coefficients (both FFE and DFE)
coeff = coeff - mu_mat*obj.error*conj(x_d);
if 1 %mu_mat ~= 0
obj.e_dc = obj.e_dc - obj.mu_dc_dd * obj.error;
obj.error_log(ddloop,m) = obj.e_dc.^2;
end
% Append new decision to decision feedback
if obj.Nb(1) > 0
%shift up one index
d_feedback(2:end) = d_feedback(1:end-1);
%replace 1st index with current estimation
d_feedback(1) = d_hat(k);
%build memorylike VNLE version
d_vnle = obj.calcVNLENonlinVecs(d_feedback,obj.Ib2,obj.Ib3,obj.Nb,obj.d_norm);
end
end
end
obj.y_out = (circshift( y.' ,-(obj.delay))).';
end
%% Functions needed During Adaption
function x_in_vnle_format = calcVNLENonlinVecs(~,x_in_block,I_2,I_3,N_,norm_)
% These are the second and third order input signal products of the VNLE EQ
% ∑ h1 x_in(k-n1) + ∑∑ h2 x_in(k-n1)*x_in(k-n2) + ∑∑∑ h3 x_in(k-n1)*x_in(k-n2)*x_in(k-n3)
x1 = x_in_block;
x2 = [];
x3 = [];
if N_(2) > 0
delta_2 = round((N_(1)-N_(2))/2);
input_vec_se = x_in_block(delta_2:end)/norm_(2); %TODO normalization step
x2 = input_vec_se(I_2(:,1)).*input_vec_se(I_2(:,2));
end
if N_(3) > 0
delta_3 = round((N_(1)-N_(3))/2);
input_vec_th = x_in_block(delta_3:end)/norm_(3);
x3 = input_vec_th(I_3(:,1)).*input_vec_th(I_3(:,2)).*input_vec_th(I_3(:,3));
end
x_in_vnle_format = [x1;x2;x3];
end
%% Functions needed for Preparation
function [C] = calcVNLEMemoryLength(~,N)
%calculates the memory length of VNLE
C = zeros(size(N));
for o = 1:numel(N)
switch o
case 1
C(o) = N(o);
case 2
C(o) = N(o)*(N(o)+1) / 2;
case 3
C(o) = N(o)*(N(o)+1)*(N(o)+2) / 6;
end
end
end
function [indvec2nd, indvec3rd] = calcIndiceVectors(~,N)
% Init vectors of 2nd and 3rd order coefficient indices ->
% yield combination with
for order = 2:numel(N)
n = N(order);
v = 1:n; % Ursprünglicher Vektor
row = 1;
% Schleifen zur Generierung des Indize Vektors
switch order
case 2
indvec2nd = zeros(n*(n+1)/2, order);
for i = 1:n
for j = i:n
indvec2nd(row, :) = [v(i) v(j)];
row = row + 1;
end
end
case 3
indvec3rd = zeros(n*(n+1)*(n+2)/6, 3);
for i = 1:n
for j = i:n
for k = j:n
indvec3rd(row, :) = [v(i) v(j) v(k)];
row = row + 1;
end
end
end
end
end
end
function powerNorm = calcPowerNormalization(~,v)
powerNorm(1) = sqrt(mean(abs(v ).^2));
powerNorm(2) = sqrt(mean(abs(v.^2).^2));
powerNorm(3) = sqrt(mean(abs(v.^3).^2));
end
end
end
classdef EQ_silas_plain < handle
%EQ_SILAS FFE and DFE Equalizer Playground
properties
% Important Signals
x_in %Input Sequence to be equalized
x_length
x_norm
d %reference signal
d_norm
d_constellation %constellation points of the reference
y_out %equalizer output signal
% FFE coefficients always named with "e"
Ne
Ce %memory length FFE
Ie1 %Indice Combination of 1nd order FFE
Ie2 %Indice Combination of 2nd order FFE
Ie3 %Indice Combination of 3nd order FFE
e %coefficients for FFE
% DFE coefficients always named with "b"
Nb
Cb %memory length DFE
Ib1 %Indice Combination of 1nd order DFE
Ib2 %Indice Combination of 2nd order DFE
Ib3 %Indice Combination of 3nd order DFE
b %coefficients for DFE
error
e_ffe
e_dfe
e_dc
error_log
mu_dc_train
mu_ffe_train
mu_dfe_train
mu_dc_dd
mu_combined_dd
delay
trainlength
sps
trainloops
ddloops
end
methods
function obj = EQ_silas_plain(options)
%EQ_SILAS Construct an instance of this class
arguments(Input)
options.Ne = [50 5 0] %Number of FFE coefficients (1st, 2nd and 3rd order)
options.Nb = [30 5 3] %Number of DFE coefficients (1st, 2nd and 3rd order)
options.trainloops = 2;
options.trainlength = 4096;
options.ddloops = 2;
options.delay = 0;
options.sps = 2;
options.mu_dc_train = 0.01;
options.mu_ffe_train = 0.005;
options.mu_dfe_train = 0.005;
options.mu_dc_dd = 0.01;
options.mu_combined_dd = [0.0004 0.0005 0.0006 0.0007 ];
end
fn = fieldnames(options);
for n = 1:numel(fn)
obj.(fn{n}) = options.(fn{n});
end
% Generate helpful vectors and initialize the filters with
% correct length:
obj.Ce = obj.calcVNLEMemoryLength(obj.Ne);
[obj.Ie2,obj.Ie3] = obj.calcIndiceVectors(obj.Ne);
obj.e = zeros(sum(obj.Ce),1);
obj.Cb = obj.calcVNLEMemoryLength(obj.Nb);
[obj.Ib2,obj.Ib3] = obj.calcIndiceVectors(obj.Nb);
obj.b = zeros(sum(obj.Cb),1);
end
function [signalclass_out,error_log] = process(obj,signalclass_in, reference_signalclass_in)
assert(signalclass_in.fs / reference_signalclass_in.fs == obj.sps)
% actual processing of the signal (steps 1. - 3.)
% 1 normalize RMS
signalclass_in = signalclass_in.normalize("mode","rms");
% Process the EQ optimization
obj.process_(signalclass_in.signal', reference_signalclass_in.signal');
signalclass_in.signal = obj.y_out';
%change sampling frequency of outgoing signal
signalclass_in.fs = reference_signalclass_in.fs;
% append to logbook
lbdesc = ['EQ von Silas ist gelaufen '];
signalclass_in = signalclass_in.logbookentry(lbdesc);
% write to output
signalclass_out = signalclass_in;
end
function process_(obj,x_in,d_in)
% 1) prepare signals
obj.e_dc = mean(x_in);
% 1.1) Input Signal
obj.x_in = [zeros(1,floor(obj.Ne(1)/2)) x_in zeros(1,obj.Ne(1))];
obj.x_length = length(x_in);
obj.x_norm = obj.calcPowerNormalization(x_in);
% 1.2 Reference Signal // Constellation
obj.d = [zeros(1,obj.Nb(1)-1) d_in zeros(1,obj.Nb(1))];
obj.d_constellation = unique(d_in);
obj.d_norm = obj.calcPowerNormalization(d_in);
% 1.3 Training
obj.trainingMode();
% 1.4 Decision Directed Mode
obj.decisionDirectedMode();
end
%% Adaptive Equalization Modes
function trainingMode(obj)
for tloop = 1:obj.trainloops
m = 1+obj.delay;
cnt = 1;
for n = obj.sps*obj.delay+1:obj.sps:obj.sps*obj.trainlength
m = m+1;
%get Sigal input vectors with correct length for VNLE
x_in_block = obj.x_in(obj.Ne(1)+n+(obj.sps-1):-1:n+obj.sps).';
x_in_vnle_format = obj.calcVNLENonlinVecs(x_in_block,obj.Ie2,obj.Ie3,obj.Ne,obj.x_norm);
%get Reference input vectors with correct length for VNLE
d_block = obj.d(obj.Nb(1)-obj.delay+m-2:-1:m-obj.delay-1).';
d_vnle_format = obj.calcVNLENonlinVecs(d_block,obj.Ib2,obj.Ib3,obj.Nb,obj.d_norm);
% Calculate the Error
obj.e_ffe = obj.e.' * x_in_vnle_format;
obj.e_dfe = obj.b.' * d_vnle_format;
obj.error = obj.e_dc + obj.e_ffe - obj.e_dfe - obj.d(obj.Nb(1)-1+m-obj.delay);
obj.error_log(tloop,cnt) = obj.error;
%update FFE coefficients with LMS
obj.e = obj.e - obj.error*conj(x_in_vnle_format)*obj.mu_ffe_train;
%update DFE coefficients with LMS
obj.b = obj.b + obj.mu_dfe_train*obj.error*d_vnle_format;
%update DC error
obj.e_dc = obj.e_dc - obj.error .* obj.mu_dc_train;
cnt = cnt+1;
end
end
end
function decisionDirectedMode(obj)
%start the dd mode with coefficients from training
coeff = [obj.e;obj.b];
for ddloop = 1:obj.ddloops
m = 0;
if all(obj.mu_combined_dd == obj.mu_combined_dd(1))
mu_mat = obj.mu_combined_dd(1);
else
mu_mat = diag([ones(1,obj.Ce(1))*obj.mu_combined_dd(1)... %1st order ffe
ones(1,obj.Ce(2))*obj.mu_combined_dd(2)... %2nd order ffe
ones(1,obj.Ce(3))*obj.mu_combined_dd(3)... %3rd order ffe
ones(1,sum(obj.Cb))*obj.mu_combined_dd(4)]); %all order dfe
end
y = zeros(1,floor(obj.x_length/obj.sps));
d_feedback = zeros(obj.Cb(1),1);
d_vnle = obj.calcVNLENonlinVecs(d_feedback,obj.Ib2,obj.Ib3,obj.Nb,obj.d_norm);
d_hat = zeros(obj.x_length,1);
for k = 1:obj.sps:obj.x_length
m=m+1;
%get Sigal input vectors with correct length for VNLE
x = obj.x_in(obj.Ne(1)+k-1:-1:k).';
x_vnle = obj.calcVNLENonlinVecs(x,obj.Ie2,obj.Ie3,obj.Ne,obj.x_norm);
%combine FFE with DFE to one vector (cursor between the two sequences)
x_d = [x_vnle;-d_vnle];
%Apply filter
y(m) = obj.e_dc + x_d.'* coeff;
%Decision
[~,symbol_idx] = min(abs(y(m) - obj.d_constellation)); % decision for closest constellation point
d_hat(k) = obj.d_constellation(symbol_idx);
%Error between FFE & DFE filtered signal and Decision
obj.error = y(m) - d_hat(k);
%Update coefficients (both FFE and DFE)
coeff = coeff - (mu_mat * (obj.error * conj(x_d)));
if 1 %mu_mat ~= 0
obj.e_dc = obj.e_dc - obj.mu_dc_dd * obj.error;
end
% Append new decision to decision feedback
if obj.Nb(1) > 0
%shift up one index
d_feedback(2:end) = d_feedback(1:end-1);
%replace 1st index with current estimation
d_feedback(1) = d_hat(k);
%build memorylike VNLE version
d_vnle = obj.calcVNLENonlinVecs(d_feedback,obj.Ib2,obj.Ib3,obj.Nb,obj.d_norm);
end
end
end
obj.y_out = (circshift( y.' ,-(obj.delay))).';
end
%% Functions needed During Adaption
function x_in_vnle_format = calcVNLENonlinVecs(~,x_in_block,I_2,I_3,N_,norm_)
% These are the second and third order input signal products of the VNLE EQ
% ∑ h1 x_in(k-n1) + ∑∑ h2 x_in(k-n1)*x_in(k-n2) + ∑∑∑ h3 x_in(k-n1)*x_in(k-n2)*x_in(k-n3)
l1=length(x_in_block);
l2=length(I_2);
l3=length(I_3);
final_length = l1+l2+l3;
x_in_vnle_format = zeros(final_length,1);
idx = l1;
x_in_vnle_format(1:idx) = x_in_block;
if N_(2) > 0
delta_2 = round((N_(1)-N_(2)) / 2);
input_vec_se = x_in_block(delta_2:end) / norm_(2); %TODO normalization step
% Extract columns from I_2
col1 = input_vec_se(I_2(:,1));
col2 = input_vec_se(I_2(:,2));
x2 = col1 .* col2;
x_in_vnle_format(idx+1:idx+l2) = x2;
end
if N_(3) > 0
delta_3 = round((N_(1)-N_(3))/2);
input_vec_th = x_in_block(delta_3:end) / norm_(3);
% Extract columns from I_3
col1 = input_vec_th(I_3(:,1));
col2 = input_vec_th(I_3(:,2));
col3 = input_vec_th(I_3(:,3));
% Perform matrix multiplication
x3 = col1 .* col2 .* col3;
idx = idx+l2;
x_in_vnle_format(idx+1:idx+l3) = x3;
end
end
%% Functions needed for Preparation
function [C] = calcVNLEMemoryLength(~,N)
%calculates the memory length of VNLE
C = zeros(size(N));
for o = 1:numel(N)
switch o
case 1
C(o) = N(o);
case 2
C(o) = N(o)*(N(o)+1) / 2;
case 3
C(o) = N(o)*(N(o)+1)*(N(o)+2) / 6;
end
end
end
function [indvec2nd, indvec3rd] = calcIndiceVectors(~,N)
% Init vectors of 2nd and 3rd order coefficient indices ->
% yield combination with
for order = 2:numel(N)
n = N(order);
v = 1:n; % Ursprünglicher Vektor
row = 1;
% Schleifen zur Generierung des Indize Vektors
switch order
case 2
indvec2nd = zeros(n*(n+1)/2, order);
for i = 1:n
for j = i:n
indvec2nd(row, :) = [v(i) v(j)];
row = row + 1;
end
end
case 3
indvec3rd = zeros(n*(n+1)*(n+2)/6, 3);
for i = 1:n
for j = i:n
for k = j:n
indvec3rd(row, :) = [v(i) v(j) v(k)];
row = row + 1;
end
end
end
end
end
end
function powerNorm = calcPowerNormalization(~,v)
powerNorm(1) = sqrt(mean(abs(v ).^2));
powerNorm(2) = sqrt(mean(abs(v.^2).^2));
powerNorm(3) = sqrt(mean(abs(v.^3).^2));
end
end
end

408
Classes/04_DSP/EQ_silas_sliding_window_dc_removal.m Normal file
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@@ -0,0 +1,408 @@
classdef EQ_silas_sliding_window_dc_removal < handle
%EQ_SILAS FFE and DFE Equalizer Playground
properties
% Important Signals
x_in %Input Sequence to be equalized
x_length
x_norm
d %reference signal
d_norm
d_constellation %constellation points of the reference
y_out %equalizer output signal
% FFE coefficients always named with "e"
Ne
Ce %memory length FFE
Ie1 %Indice Combination of 1nd order FFE
Ie2 %Indice Combination of 2nd order FFE
Ie3 %Indice Combination of 3nd order FFE
e %coefficients for FFE
% DFE coefficients always named with "b"
Nb
Cb %memory length DFE
Ib1 %Indice Combination of 1nd order DFE
Ib2 %Indice Combination of 2nd order DFE
Ib3 %Indice Combination of 3nd order DFE
b %coefficients for DFE
error
e_ffe
e_dfe
e_dc
% coefficients
mu_dc_train
mu_ffe_train
mu_dfe_train
mu_dc_dd
mu_ffe_dd
mu_dfe_dd
mu_combined_dd % [1st order FFE, 2nd order FFE, 3rd order FFE, all orders DFE]
delay
trainlength
sps
trainloops
ddloops
eq_blocklength % block lengt of EQ (until now, only the dc subtraction is affected by this)
eq_updatelatency % time in symbols until the calculated updates reach the signal again (until now, only the dc subtraction is affected by this)
end
methods
function obj = EQ_silas_sliding_window_dc_removal(options)
%EQ_SILAS Construct an instance of this class
arguments(Input)
options.Ne = [50 5 0] %Number of FFE coefficients (1st, 2nd and 3rd order)
options.Nb = [30 5 3] %Number of DFE coefficients (1st, 2nd and 3rd order)
options.trainloops = 2;
options.trainlength = 4096;
options.ddloops = 2;
options.delay = 0;
options.sps = 2;
options.mu_dc_train = 0.01;
options.mu_ffe_train = 0.005;
options.mu_dfe_train = 0.005;
options.mu_dc_dd = 0.01;
options.mu_ffe_dd = [0.0004 0.0005 0.0006];
options.mu_dfe_dd = 0.0005;
options.eq_blocklength = 1;
options.eq_updatelatency = 1;
end
fn = fieldnames(options);
for n = 1:numel(fn)
obj.(fn{n}) = options.(fn{n});
end
% Generate helpful vectors and initialize the filters with
% correct length:
obj.Ce = obj.calcVNLEMemoryLength(obj.Ne);
[obj.Ie2,obj.Ie3] = obj.calcIndiceVectors(obj.Ne);
obj.e = zeros(sum(obj.Ce),1);
obj.Cb = obj.calcVNLEMemoryLength(obj.Nb);
[obj.Ib2,obj.Ib3] = obj.calcIndiceVectors(obj.Nb);
obj.b = zeros(sum(obj.Cb),1);
end
function [signalclass_out] = process(obj,signalclass_in, reference_signalclass_in)
% actual processing of the signal (steps 1. - 3.)
% 1 normalize RMS
%signalclass_in = signalclass_in.normalize("mode","rms");
% Process the EQ optimization
obj.process_(signalclass_in.signal', reference_signalclass_in.signal');
signalclass_in.signal = obj.y_out';
% append to logbook
lbdesc = ['EQ von Silas ist gelaufen '];
signalclass_in = signalclass_in.logbookentry(lbdesc);
% write to output
signalclass_out = signalclass_in;
end
function process_(obj,x_in,d_in)
% 1) prepare signals
obj.e_dc = mean(x_in);
% 1.1) Input Signal
obj.x_in = [zeros(1,floor(obj.Ne(1)/2)) x_in zeros(1,obj.Ne(1))];
obj.x_length = length(x_in);
obj.x_norm = obj.calcPowerNormalization(x_in);
% 1.2 Reference Signal // Constellation
obj.d = [zeros(1,obj.Nb(1)-1) d_in zeros(1,obj.Nb(1))];
obj.d_constellation = unique(d_in);
obj.d_norm = obj.calcPowerNormalization(d_in);
% 1.3 Training
obj.trainingMode();
% 1.4 Decision Directed Mode
obj.decisionDirectedMode();
end
%% Adaptive Equalization Modes
function trainingMode(obj)
dc_avg_block = zeros(obj.eq_blocklength,1);
for tloop = 1:obj.trainloops
m = 1+obj.delay;
for n = obj.sps*obj.delay+1:obj.sps:obj.sps*obj.trainlength
m = m+1;
%get Sigal input vectors with correct length for VNLE
x_in_block = obj.x_in(obj.Ne(1)+n+(obj.sps-1):-1:n+obj.sps).';
x_in_vnle_format = obj.calcVNLENonlinVecs(x_in_block,obj.Ie2,obj.Ie3,obj.Ne,obj.x_norm);
%get Reference input vectors with correct length for VNLE
d_block = obj.d(obj.Nb(1)-obj.delay+m-2:-1:m-obj.delay-1).';
d_vnle_format = obj.calcVNLENonlinVecs(d_block,obj.Ib2,obj.Ib3,obj.Nb,obj.d_norm);
obj.e_ffe = obj.e.' * x_in_vnle_format;
dc_avg_block = circshift(dc_avg_block,1);
dc_avg_block(1) = obj.e_ffe;
if n > obj.eq_blocklength * obj.sps
obj.e_dc = obj.e_ffe - mean(dc_avg_block).* obj.mu_dc_train;
else
obj.e_dc = 0;
end
obj.e_dfe = obj.b.' * d_vnle_format;
% Calculate the Error
obj.error = obj.e_ffe - obj.e_dfe - obj.d(obj.Nb(1)-1+m-obj.delay);
if obj.mu_ffe_train ~= 0
%update FFE coefficients with LMS
obj.e = obj.e - obj.error*conj(x_in_vnle_format)*obj.mu_ffe_train;
else
%update FFE coefficients with NLMS
obj.e = obj.e - obj.error*x_in_vnle_format/(x_in_vnle_format.'*x_in_vnle_format);
end
%update DFE coefficients with LMS
obj.b = obj.b + obj.mu_dfe_train*obj.error*d_vnle_format;
end
end
end
function decisionDirectedMode(obj)
%start the dd mode with coefficients from training
coeff = [obj.e;obj.b];
for ddloop = 1:obj.ddloops
dc_avg_block = zeros(obj.eq_blocklength,1);
m = 0;
dc_cnt = 0;
mu_mat = diag([ones(1,obj.Ce(1))*obj.mu_ffe_dd(1)... %1st order ffe
ones(1,obj.Ce(2))*obj.mu_ffe_dd(2)... %2nd order ffe
ones(1,obj.Ce(3))*obj.mu_ffe_dd(3)... %3rd order ffe
ones(1,sum(obj.Cb))*obj.mu_dfe_dd]); %all order dfe
mu_ffe = [ones(1,obj.Ce(1))*obj.mu_ffe_dd(1)... %1st order ffe
ones(1,obj.Ce(2))*obj.mu_ffe_dd(2)... %2nd order ffe
ones(1,obj.Ce(3))*obj.mu_ffe_dd(3)];
mu_dfe = ones(1,sum(obj.Cb))*obj.mu_dfe_dd;
y = zeros(1,floor(obj.x_length/obj.sps));
y_= zeros(1,floor(obj.x_length/obj.sps));
d_feedback = zeros(obj.Cb(1),1);
d_vnle = obj.calcVNLENonlinVecs(d_feedback,obj.Ib2,obj.Ib3,obj.Nb,obj.d_norm);
d_hat = zeros(obj.x_length,1);
for k = 1:obj.sps:obj.x_length
dc_cnt = dc_cnt+1;
m=m+1;
%get Sigal input vectors with correct length for VNLE
x = obj.x_in(obj.Ne(1)+k-1:-1:k).';
x_vnle = obj.calcVNLENonlinVecs(x,obj.Ie2,obj.Ie3,obj.Ne,obj.x_norm);
%combine FFE with DFE to one vector (cursor between the two sequences)
x_d = [x_vnle;-d_vnle];
%Apply filter
y_(m) = x_d.'* coeff ;
y(m) = y_(m) - obj.e_dc(end) ;
dc_avg_block = circshift(dc_avg_block,1);
dc_avg_block(1) = y(m);
obj.e_dc(m) = (y(m) - mean(dc_avg_block)) .* obj.mu_dc_dd;
%Decision
[~,symbol_idx] = min(abs(y(m) - obj.d_constellation)); % decision for closest constellation point
d_hat(k) = obj.d_constellation(symbol_idx);
%Error between FFE & DFE filtered signal and Decision
obj.error = y(m) - d_hat(k);
%Update coefficients (both FFE and DFE)
% obj.e = obj.e - obj.error * mu_ffe * conj(x_vnle);
% obj.b = obj.b + obj.error * mu_dfe * conj(d_vnle);
% coeff = [obj.e;obj.b];
coeff = coeff - mu_mat*obj.error*conj(x_d);
% Append new decision to decision feedback
if obj.Nb(1) > 0
%shift up one index
d_feedback(2:end) = d_feedback(1:end-1);
%replace 1st index with current estimation
d_feedback(1) = d_hat(k);
%build memorylike VNLE version
d_vnle = obj.calcVNLENonlinVecs(d_feedback,obj.Ib2,obj.Ib3,obj.Nb,obj.d_norm);
end
end
end
obj.y_out = (circshift( y.' ,-(obj.delay))).';
end
%% Functions needed During Adaption
function x_in_vnle_format = calcVNLENonlinVecs(~,x_in_block,I_2,I_3,N_,norm_)
% These are the second and third order input signal products of the VNLE EQ
% ∑ h1 x_in(k-n1) + ∑∑ h2 x_in(k-n1)*x_in(k-n2) + ∑∑∑ h3 x_in(k-n1)*x_in(k-n2)*x_in(k-n3)
x1 = x_in_block;
x2 = [];
x3 = [];
if N_(2) > 0
delta_2 = round((N_(1)-N_(2))/2);
input_vec_se = x_in_block(delta_2:end)/norm_(2); %TODO normalization step
x2 = input_vec_se(I_2(:,1)).*input_vec_se(I_2(:,2));
end
if N_(3) > 0
delta_3 = round((N_(1)-N_(3))/2);
input_vec_th = x_in_block(delta_3:end)/norm_(3);
x3 = input_vec_th(I_3(:,1)).*input_vec_th(I_3(:,2)).*input_vec_th(I_3(:,3));
end
x_in_vnle_format = [x1;x2;x3];
end
%% Functions needed for Preparation
function [C] = calcVNLEMemoryLength(~,N)
%calculates the memory length of VNLE
C = zeros(size(N));
for o = 1:numel(N)
switch o
case 1
C(o) = N(o);
case 2
C(o) = N(o)*(N(o)+1) / 2;
case 3
C(o) = N(o)*(N(o)+1)*(N(o)+2) / 6;
end
end
end
function [indvec2nd, indvec3rd] = calcIndiceVectors(~,N)
% Init vectors of 2nd and 3rd order coefficient indices ->
% yield combination with
for order = 2:numel(N)
n = N(order);
v = 1:n; % Ursprünglicher Vektor
row = 1;
% Schleifen zur Generierung des Indize Vektors
switch order
case 2
indvec2nd = zeros(n*(n+1)/2, order);
for i = 1:n
for j = i:n
indvec2nd(row, :) = [v(i) v(j)];
row = row + 1;
end
end
case 3
indvec3rd = zeros(n*(n+1)*(n+2)/6, 3);
for i = 1:n
for j = i:n
for k = j:n
indvec3rd(row, :) = [v(i) v(j) v(k)];
row = row + 1;
end
end
end
end
end
end
function powerNorm = calcPowerNormalization(~,v)
powerNorm(1) = sqrt(mean(abs(v ).^2));
powerNorm(2) = sqrt(mean(abs(v.^2).^2));
powerNorm(3) = sqrt(mean(abs(v.^3).^2));
end
end
end

57
Functions/calc_ber.m
View File

@@ -1,37 +1,58 @@
function [bits,errors,ber,errorLocation] = calc_ber(data_in,data_ref,options)
function [bits,errors,ber,errorIndice] = calc_ber(data_in,data_ref,options)
arguments(Input)
data_in;
data_ref;
options.skip = 0;
options.skip_front = 0;
options.skip_end = 0;
options.returnErrorLocation = 0;
end
options.skip_end = abs(options.skip_end);
options.skip_front = abs(options.skip_front);
bits = 0;
errors= 0;
ber= 0;
errorLocation= [];
errorIndice= [];
data_ref = logical(data_ref)';
data_in = logical(data_in)';
% trim sequence to given start; stop. trim reference if signal is shorter
[data_in,data_ref]=trimseq(data_in,data_ref,options.skip_front,options.skip_end);
bits = 0;
bits = bits+numel(data_in(:,options.skip+1:end));
if length(data_ref) == length(data_in)
try
if options.returnErrorLocation == 0
errors = sum( data_in(:,options.skip+1:end) ~= data_ref(:,options.skip+1:end),"all" );
else
errorLocation = sum(data_in(:,options.skip+1:end) ~= data_ref(:,options.skip+1:end),1);
errors = sum(errorLocation ,"all" );
bits = 0;
bits = bits+numel(data_in(:,options.skip_front+1:end));
try
if options.returnErrorLocation == 0
errors = sum( data_in ~= data_ref,"all" );
else
errorIndice = sum(data_in ~= data_ref,1);
errors = sum(errorIndice ,"all" );
[~,errorIndice] = find(errorIndice==1);
end
catch
warning('BER calculation not optimal: Arrays have incompatible sizes for this operation.')
errors = NaN;
end
catch
%warning('BER calculation not optimal: Arrays have incompatible sizes for this operation.')
errors = NaN;
% Determine BER
ber = sum(errors)/sum(bits);
else
errormsg('Sequence length does not match');
end
% Determine BER
ber = sum(errors)/sum(bits);
function [data_,reference_]=trimseq(data,reference,skipstart,skip_end)
data_ = logical(data(skipstart+1:end-skip_end,:))';
delta_bits = length(reference) - length(data_);
skip_end = max(skip_end,delta_bits);
reference_ = logical(reference(skipstart+1:end-skip_end,:))';
end
end

120
MPI_offline_analysis/mpi_analysis_pam4.m
View File

@@ -1,120 +0,0 @@
clear;
col = linspecer(6);
if ismac
foldername = '/Users/silasoettinghaus/Documents/MATLAB/Labor_Datensatz_PAM4_MPI/pam4_10km_1km';
else
foldername = 'C:\Users\Silas\Documents\MATLAB\Labor_Datensatz_PAM4_MPI\pam4_10km_1km';
end
allfiles = dir(foldername);
mudc_loop = [0.05 0.01 0.005];
parfsym = zeros(length(mudc_loop),length(allfiles));
parsir = zeros(length(mudc_loop),length(allfiles));
ber_wo_reduction = zeros(length(mudc_loop),length(allfiles));
ber_w_dcremoval = zeros(length(mudc_loop),length(allfiles));
mudc = zeros(length(mudc_loop),length(allfiles));
for j = 1:length(mudc_loop)
mudc_current = mudc_loop(j);
parfor i = 1:length(allfiles)
if allfiles(i).bytes ~= 0
current_filename = allfiles(i).name;
else
continue
end
% load dataset
simdata = load([foldername,filesep, current_filename]);
% get common variables of simulation loop
%even if redundant, save stuff to results struct
parsir(j,i) = simdata.saveStructTemp.awg2scope_keysight_state.eigenlight_mpi - 3.3 + 7.2;
parfsym(j,i) = simdata.saveStructTemp.common.f_sym;
fdac = simdata.saveStructTemp.common.f_DAC;
fadc = simdata.saveStructTemp.common.f_ADC;
fsym = simdata.saveStructTemp.common.f_sym;
% 0) build RX Signal
y_rx = Electricalsignal(simdata.saveStructTemp.dp_tsynch_out');
y_rx.fs = 2.*fsym;
% 0) build tx reference Signal for eq training
y_digimod = Electricalsignal(simdata.saveStructTemp.digi_mod_out');
y_digimod.fs = fsym;
% 1) normlaize
y_rx = y_rx.normalize("mode","rms");
eq = EQ_silas("Ne",[50,9,9],"Nb",[2,0,0],"trainlength",4096,...
"sps",2,...
"mu_dc_dd",mudc_current,...
"mu_dc_train",mudc_current,...
"mu_ffe_train",0,...
"mu_dfe_train",0.005,...
"mu_ffe_dd",[0.0004 0.0004 0.0004],...
"mu_dfe_dd",0.005,...
"ddloops",4,...
"trainloops",4,...
"eq_blocklength",10);
[Eq_out] = eq.process(y_rx,y_digimod);
% 2.2) MPI reduction blocks
% 3) digital demodulation
digimod = PAMmapper(2^simdata.saveStructTemp.common.M,0);
d_hat = digimod.demap(Eq_out);
d_ = digimod.demap(Electricalsignal(simdata.saveStructTemp.digi_mod_out'));
d = simdata.saveStructTemp.prms_out;
% 4) BER calculation
[totalbits,errors_bm,ber_dhat,loc] = calc_ber(d_hat.signal(1:end,:) ,d(1:end,:)',"skip",0,"returnErrorLocation",1);
ber_nodctap = mean(simdata.saveStructTemp.prms_compare_state.BER );
disp(['fsym: ',num2str(parfsym(j,i).*1e-9),'GBd, SIR: ',num2str(parsir(j,i)),' ->> BER_new: ',sprintf('%2E',ber_dhat),' BER_old: ',sprintf('%2E',ber_nodctap)]);
% 5) save to struct
ber_wo_reduction(j,i) = ber_nodctap;
ber_w_dcremoval(j,i) = ber_dhat;
mudc(j,i) = eq.mu_dc_dd;
end
end
rate = [92e9, 56e9];
figure(99)
for j = 1:length(mudc_loop)
for r = 1:length(rate)
a = [parsir(j,parfsym(j,:)==rate(r));ber_wo_reduction(j,parfsym(j,:)==rate(r))];
a=sort(a,2);
plot(a(1,:),a(2,:),'DisplayName',[num2str(rate(r).*1e-9),' GBd; PAM4; w/o reduction'],'LineWidth',2,'Marker','o','MarkerSize',3);
%scatter(parsir(parfsym==rate(r)),ber_wo_reduction(parfsym==rate(r)),'DisplayName',[num2str(rate(r).*1e-9),' GBd; PAM4; w/o reduction'],'LineWidth',2,'Marker','o')
hold on
b = [parsir(j,parfsym(j,:)==rate(r));ber_w_dcremoval(j,parfsym(j,:)==rate(r))];
b=sort(b,2);
plot(b(1,:),b(2,:),'DisplayName',[num2str(rate(r).*1e-9),' GBd; PAM4; adaptive DC removal'],'LineWidth',2,'Marker','+','MarkerSize',3);
%scatter(parsir(parfsym==rate(r)),ber_w_dcremoval(parfsym==rate(r)),'DisplayName',[num2str(rate(r).*1e-9),' GBd; PAM4; adaptive DC removal'],'LineWidth',2,'Marker','+');
end
end
set(gca, 'YScale', 'log');
yline(3.8e-3,'LineWidth',1.5);

140
filter_debug.m → projects/IMDD_base_system/filter_debug.m
View File

@@ -1,70 +1,70 @@
clear
%% Set Simulation Variables
O = 18; %order of prbs
N = 2^(O-1); %length of prbs
[~,seed] = prbs(O,1); %initialize first seed of prbs
% Modulation
M = 4; %PAM-M
bitpattern = zeros(N,log2(M));
% Symbol Rate
fsym = 112e9;
% DAC Rate
fdac = 120e9;
% Simulation oversampling rate "k";
kover = 16;
% ADC Rate
fadc = 256e9;
% Simulation frequency in "analog domain"
fsimu = kover * fdac ;
%% CONSTRUCT ALL CLASSES
digimod = PAMmapper(M,0);
pulsef = Pulseformer("pulseform","rrc","fdac",fdac,"fsym",fsym,"pulselength",32,"rrcalpha",0.05);
awg = AWG('preset','M8199B','fdac',fdac,'kover',kover,'lpf_active',1,'f_cutoff',56e9,'lpf_type',filtertypes.gaussian,'bit_resolution',5.5);
lp_laser = Filter('filtdegree',1,"f_cutoff",30e9,"fsamp",fdac,"filterType",filtertypes.bessel_inp);
%% PROCESS
% PRBS Generation
for i = 1:log2(M)
[bitpattern(:,i),seed] = prbs(O,N,seed);
end
% Build Inf. signal class
bits = Informationsignal(bitpattern);
% Digi Mod
mod_out = digimod.map(bits);
% merken für EQ training
reference = mod_out;
% shape shape
mod_out = pulsef.process(mod_out);
test = applyPulseShaping(reference.signal,fsym,fdac);
% AWG -> ELECTRICAL DOMAIN
X = lp_laser.process(mod_out);
%X.spectrum(fsimu,"displayname",'AWG out','figurename','after AWG');
clear
%% Set Simulation Variables
O = 18; %order of prbs
N = 2^(O-1); %length of prbs
[~,seed] = prbs(O,1); %initialize first seed of prbs
% Modulation
M = 4; %PAM-M
bitpattern = zeros(N,log2(M));
% Symbol Rate
fsym = 112e9;
% DAC Rate
fdac = 120e9;
% Simulation oversampling rate "k";
kover = 16;
% ADC Rate
fadc = 256e9;
% Simulation frequency in "analog domain"
fsimu = kover * fdac ;
%% CONSTRUCT ALL CLASSES
digimod = PAMmapper(M,0);
pulsef = Pulseformer("pulseform","rrc","fdac",fdac,"fsym",fsym,"pulselength",32,"rrcalpha",0.05);
awg = AWG('preset','M8199B','fdac',fdac,'kover',kover,'lpf_active',1,'f_cutoff',56e9,'lpf_type',filtertypes.gaussian,'bit_resolution',5.5);
lp_laser = Filter('filtdegree',1,"f_cutoff",30e9,"fsamp",fdac,"filterType",filtertypes.bessel_inp);
%% PROCESS
% PRBS Generation
for i = 1:log2(M)
[bitpattern(:,i),seed] = prbs(O,N,seed);
end
% Build Inf. signal class
bits = Informationsignal(bitpattern);
% Digi Mod
mod_out = digimod.map(bits);
% merken für EQ training
reference = mod_out;
% shape shape
mod_out = pulsef.process(mod_out);
test = applyPulseShaping(reference.signal,fsym,fdac);
% AWG -> ELECTRICAL DOMAIN
X = lp_laser.process(mod_out);
%X.spectrum(fsimu,"displayname",'AWG out','figurename','after AWG');

451
imddmodel.m → projects/IMDD_base_system/imddmodel.m
View File

@@ -1,226 +1,227 @@
function output = imddmodel(sir,dcmode)
for realiz = 1:3
rng(realiz);
%% Set Simulation Variables
delay = 0;10+(10*realiz); %mpi delay in meter
fiblen = 0; %main link in km
laser_linewidth =0e6;
O = 18; %order of prbs
N = 2^(O-1); %length of prbs
[~,seed] = prbs(O,1); %initialize first seed of prbs
% Modulation
M = 4; %PAM-M
bitpattern = zeros(N,log2(M));
% Symbol Rate
fsym = 112e9;
% DAC Rate
fdac = 120e9;
% Simulation oversampling rate "k";
kover = 16;
% ADC Rate
fadc = 256e9;
% Simulation frequency in "analog domain"
fsimu = kover * fdac ;
%% B) CONSTRUCT ALL CLASSES %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
digimod = PAMmapper(M,0);
pulseform = Pulseformer("pulseform","rrc","fdac",fdac,"fsym",fsym,"pulselength",32,"rrcalpha",0.027);
awg = AWG('fdac',fdac,'kover',kover,'lpf_active',1,'f_cutoff',56e9,'lpf_type',filtertypes.gaussian,'bit_resolution',5.5);
lp_laser = Filter('filtdegree',1,"f_cutoff",50e9,"fsamp",fdac*kover,"filterType",filtertypes.bessel_inp);
u_pi = 3.5;
vbias = (0.5*u_pi)-u_pi;
extmodlaser = EML("mode",eml_mode.im_cosinus,"power",5,"fsimu",fsimu,"lambda",1550,"bias",vbias,"u_pi",u_pi,"linewidth",laser_linewidth);
fib = Fiber("fsimu",fdac*kover,"fiber_length",fiblen,"alpha",0.2,"D",16,"lambda0",thz2nm(193.1),"gamma",0);
reflectionpoint = Amplifier("amp_mode","ideal_no_noise","gain_mode","gain","amplification_db",sir);
reflectionprop = Fiber("fsimu",fdac*kover,"fiber_length",delay/1000,"alpha",0.2,"D",16,"lambda0",1550,"gamma",0);
opticatten = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power","amplification_db",-8);
phdiode = Photodiode("fsimu",fdac*kover,"dark_current",2e-08,"responsivity",1,"temperature",20);
lp_diode = Filter('filtdegree',1,"f_cutoff",50e9,"fsamp",fdac*kover,"filterType",filtertypes.bessel_inp);
scp = Scope("fsimu",fdac*kover,"fadc",fadc,...
"delay",0,"fixed_delay",0,"lpf_bw",113e9,"filtertype",filtertypes.butterworth,...
"samplingdelay",0,"rand_samplingdelay",0,"freq_offset",0,"samp_jitter",0,...
"adcresolution",6,"quantbuffer",0.1,'block_dc',1);
eq = EQ("K",2,"plottrain",0,"plotfinal",0,...
"training_length",4096,"training_loops",2,...
"Ne",[50,5,3],"Nb",[3,2,2],...
"DCmu",0.05,"DDmu",[0.0004 0.0005 0.0006 0.0007 ],"DFEmu",0.005,"FFEmu",0.005,...
"dd_loops",2,"epsilon",[10 100 1000 ],"M",2,...
"thres",[0.005 0.004 0.0005 ],"l1act",0,"delay",0,"rho",0.0005,"ideal_dfe",0,"DB_aim",0);
eq2 = EQ_silas("Ne",[20,0,0],"Nb",[3,0,0],"trainlength",4096,"mu_dc_dd",0.005,"mu_dc_train",0.05,...
"mu_ffe_train",0.005,"mu_combined_dd",[0.0004 0.0006 0.0003 0.005],"ddloops",3,"dcmode",dcmode);
%% C) PROCESS TX %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 1) PRBS Generation
for i = 1:log2(M)
[bitpattern(:,i),seed] = prbs(O,N,seed);
end
% 2 ) Build Inf. signal class
bits = Informationsignal(bitpattern);
% 3) Digi modulation -> PAM-M signal
digimod_out = digimod.map(bits);
% 4) Pulse shaping -> racos
X = pulseform.process(digimod_out);
% 5) AWG (lowpass, quantization, sample and hold)
X = awg.process(X);
% 6) Lowpass behavior of laser and hf-cable? why twice?
X = lp_laser.process(X);
X = lp_laser.process(X);
% 7) Normalize signal
X = X.normalize("mode","oneone");
X.signal = X.signal .* 1.3800;
%% D) PROCESS OPTICAL CHANNEL %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 1) Laser; Modulation -> OPTICAL DOMAIN
[X,extmodlaser] = extmodlaser.process(X);
% 2) Main fiber propagation
X = fib.process(X);
if delay ~= 0
% 3) Reflection
% Reflection is just an attenuation
R = reflectionpoint.process(X);
% Propagate back and forth (actual fiber propagation)
R = reflectionprop.process(R);
% Delay the reflected signal
[R,n] = R.delay("delay_meter",delay);
% Add together
X.signal = X.signal(n:end);
R.signal = R.signal(n:end);
%disp(['SIR ',num2str(10*log10(X.power/R.power))]);
X = X+R;
% 4) Equalize
% cut reference signal to correct length (nessecary due to MPI delay)
digimod_out.signal = digimod_out.signal(round(n * fsym/fsimu) : end,:);
bitpattern = bitpattern(round(n * fsym/fsimu):end,:);
end
% 4) Attenuation
X = opticatten.process(X);
% X = edfaamp.process(X);
% 5) Photo Diode -> ELECTRICAL DOMAIN
X = phdiode.process(X);
X = lp_diode.process(X);
%% E) PROCESS RX %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 1) Oscilloscope (Sampling to f_adc; Quantization; Bandwidth Limitation)
X = scp.process(X);
% 2) Resample to 2x symbol rate
X = X.resample("fs_out",2*fsym,"fs_in",fadc);
% 3) Normalize
Eq_in = X.normalize("mode","rms");
% MPI reduction DC removal BEFORE EQ
wl = 3000; % symbols
Eq_in.signal = Eq_in.signal - 1/wl .* movsum( Eq_in.signal,[wl/2,wl/2]);
% Equalize Signal
[Eq_out] = eq2.process(Eq_in,digimod_out);
%% A1: MPI reduction DC removal
wl = 1000; % symbols
yk_dcsm = Eq_out;
yk_dcsm.signal = Eq_out.signal - 1/wl .* movsum( Eq_out.signal,[wl/2,wl/2]);
%% A2: MPI reduction Level wise error removal
yk_lvsm = Eq_out;
yk_lvlp = Eq_out;
pre_decision_level_uni = digimod.decide_pamlevel(Eq_out);
pre_decision_level_bi = ( pre_decision_level_uni*2-3 ) .* 1/sqrt(5);
e = Eq_out.signal - pre_decision_level_bi;
lp_mpi = Filter('filtdegree',1,"f_cutoff",2e6,"fsamp",fsym,"filterType",filtertypes.bessel_inp);
filtered = lp_mpi.process(e);
wl = 30; % symbols
smoothed = ( 1/wl .* movsum(e,[wl/2,wl/2]) );
% remove interference
for level = 0:3
yk_lvsm.signal(pre_decision_level_uni==level) = yk_lvsm.signal(pre_decision_level_uni==level) - smoothed(pre_decision_level_uni==level);
yk_lvlp.signal(pre_decision_level_uni==level) = yk_lvlp.signal(pre_decision_level_uni==level) - filtered(pre_decision_level_uni==level);
end
%% PROCESS RX %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 1) Digi Demod
d_bm = digimod.demap(Eq_out);
d_dcsm = digimod.demap(yk_dcsm);
d_lvsm = digimod.demap(yk_lvsm);
d_lvlp = digimod.demap(yk_lvlp);
% 2) BER
dbit = length(d_bm.signal)-length(bitpattern);
[~,errors_bm,ber_bm(realiz),loc] = calc_ber(d_bm.signal(1:end-dbit,:) ,bitpattern(1:end,:),"skip",0,"returnErrorLocation",1);
[~,errors_dcsm,ber_dcsm(realiz)] = calc_ber(d_dcsm.signal(1:end-dbit,:) ,bitpattern(1:end,:),"skip",0,"returnErrorLocation",0);
[~,errors_lvsm,ber_lvsm(realiz)] = calc_ber(d_lvsm.signal(1:end-dbit,:) ,bitpattern(1:end,:),"skip",0,"returnErrorLocation",0);
[~,errors_lvlp,ber_lvlp(realiz)] = calc_ber(d_lvlp.signal(1:end-dbit,:) ,bitpattern(1:end,:),"skip",0,"returnErrorLocation",0);
end
output.ber_bm = mean(ber_bm);
output.ber_dcsm = mean(ber_dcsm);
output.ber_lvsm = mean(ber_lvsm);
output.ber_lvlp = mean(ber_lvlp);
function output = imddmodel(sir,dcmode)
for realiz = 1:3
rng(realiz);
%% Set Simulation Variables
delay = 0;10+(10*realiz); %mpi delay in meter
fiblen = 0; %main link in km
laser_linewidth =0e6;
O = 18; %order of prbs
N = 2^(O-1); %length of prbs
[~,seed] = prbs(O,1); %initialize first seed of prbs
% Modulation
M = 4; %PAM-M
bitpattern = zeros(N,log2(M));
% Symbol Rate
fsym = 112e9;
% DAC Rate
fdac = 120e9;
% Simulation oversampling rate "k";
kover = 16;
% ADC Rate
fadc = 256e9;
% Simulation frequency in "analog domain"
fsimu = kover * fdac ;
%% B) CONSTRUCT ALL CLASSES %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
digimod = PAMmapper(M,0);
pulseform = Pulseformer("pulseform","rrc","fdac",fdac,"fsym",fsym,"pulselength",32,"rrcalpha",0.027);
awg = AWG('fdac',fdac,'kover',kover,'lpf_active',1,'f_cutoff',56e9,'lpf_type',filtertypes.gaussian,'bit_resolution',5.5);
lp_laser = Filter('filtdegree',1,"f_cutoff",50e9,"fsamp",fdac*kover,"filterType",filtertypes.bessel_inp);
u_pi = 3.5;
vbias = (0.5*u_pi)-u_pi;
extmodlaser = EML("mode",eml_mode.im_cosinus,"power",5,"fsimu",fsimu,"lambda",1550,"bias",vbias,"u_pi",u_pi,"linewidth",laser_linewidth);
fib = Fiber("fsimu",fdac*kover,"fiber_length",fiblen,"alpha",0.2,"D",16,"lambda0",thz2nm(193.1),"gamma",0);
reflectionpoint = Amplifier("amp_mode","ideal_no_noise","gain_mode","gain","amplification_db",sir);
reflectionprop = Fiber("fsimu",fdac*kover,"fiber_length",delay/1000,"alpha",0.2,"D",16,"lambda0",1550,"gamma",0);
opticatten = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power","amplification_db",-8);
phdiode = Photodiode("fsimu",fdac*kover,"dark_current",2e-08,"responsivity",1,"temperature",20);
lp_diode = Filter('filtdegree',1,"f_cutoff",50e9,"fsamp",fdac*kover,"filterType",filtertypes.bessel_inp);
scp = Scope("fsimu",fdac*kover,"fadc",fadc,...
"delay",0,"fixed_delay",0,"lpf_bw",113e9,"filtertype",filtertypes.butterworth,...
"samplingdelay",0,"rand_samplingdelay",0,"freq_offset",0,"samp_jitter",0,...
"adcresolution",6,"quantbuffer",0.1,'block_dc',1);
eq = EQ("K",2,"plottrain",0,"plotfinal",0,...
"training_length",4096,"training_loops",2,...
"Ne",[50,5,3],"Nb",[3,2,2],...
"DCmu",0.05,"DDmu",[0.0004 0.0005 0.0006 0.0007 ],"DFEmu",0.005,"FFEmu",0.005,...
"dd_loops",2,"epsilon",[10 100 1000 ],"M",2,...
"thres",[0.005 0.004 0.0005 ],"l1act",0,"delay",0,"rho",0.0005,"ideal_dfe",0,"DB_aim",0);
eq2 = EQ_silas("Ne",[20,0,0],"Nb",[3,0,0],"trainlength",4096,"mu_dc_dd",0.005,"mu_dc_train",0.05,...
"mu_ffe_train",0.005,"mu_combined_dd",[0.0004 0.0006 0.0003 0.005],"ddloops",3,"dcmode",dcmode);
%% C) PROCESS TX %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 1) PRBS Generation
for i = 1:log2(M)
[bitpattern(:,i),seed] = prbs(O,N,seed);
end
% 2 ) Build Inf. signal class
bits = Informationsignal(bitpattern);
% 3) Digi modulation -> PAM-M signal
digimod_out = digimod.map(bits);
% 4) Pulse shaping -> racos
X = pulseform.process(digimod_out);
% 5) AWG (lowpass, quantization, sample and hold)
X = awg.process(X);
% 6) Lowpass behavior of laser and hf-cable? why twice?
X = lp_laser.process(X);
X = lp_laser.process(X);
% 7) Normalize signal
X = X.normalize("mode","oneone");
X.signal = X.signal .* 1.3800;
%% D) PROCESS OPTICAL CHANNEL %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 1) Laser; Modulation -> OPTICAL DOMAIN
[X,extmodlaser] = extmodlaser.process(X);
% 2) Main fiber propagation
X = fib.process(X);
if delay ~= 0
% 3) Reflection
% Reflection is just an attenuation
R = reflectionpoint.process(X);
% Propagate back and forth (actual fiber propagation)
R = reflectionprop.process(R);
% Delay the reflected signal
[R,n] = R.delay("delay_meter",delay);
% Add together
X.signal = X.signal(n:end);
R.signal = R.signal(n:end);
%disp(['SIR ',num2str(10*log10(X.power/R.power))]);
X = X+R;
% 4) Equalize
% cut reference signal to correct length (nessecary due to MPI delay)
digimod_out.signal = digimod_out.signal(round(n * fsym/fsimu) : end,:);
bitpattern = bitpattern(round(n * fsym/fsimu):end,:);
end
% 4) Attenuation
X = opticatten.process(X);
% X = edfaamp.process(X);
% 5) Photo Diode -> ELECTRICAL DOMAIN
X = phdiode.process(X);
X = lp_diode.process(X);
%% E) PROCESS RX %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 1) Oscilloscope (Sampling to f_adc; Quantization; Bandwidth Limitation)
X = scp.process(X);
% 2) Resample to 2x symbol rate
X = X.resample("fs_out",2*fsym,"fs_in",fadc);
% 3) Normalize
Eq_in = X.normalize("mode","rms");
% MPI reduction DC removal BEFORE EQ
wl = 3000; % symbols
Eq_in.signal = Eq_in.signal - 1/wl .* movsum( Eq_in.signal,[wl/2,wl/2]);
% Equalize Signal
[Eq_out] = eq2.process(Eq_in,digimod_out);
%% A1: MPI reduction DC removal
wl = 1000; % symbols
yk_dcsm = Eq_out;
yk_dcsm.signal = Eq_out.signal - 1/wl .* movsum( Eq_out.signal,[wl/2,wl/2]);
%% A2: MPI reduction Level wise error removal
yk_lvsm = Eq_out;
yk_lvlp = Eq_out;
pre_decision_level_uni = digimod.decide_pamlevel(Eq_out);
pre_decision_level_bi = ( pre_decision_level_uni*2-3 ) .* 1/sqrt(5);
e = Eq_out.signal - pre_decision_level_bi;
lp_mpi = Filter('filtdegree',1,"f_cutoff",2e6,"fsamp",fsym,"filterType",filtertypes.bessel_inp);
filtered = lp_mpi.process(e);
wl = 30; % symbols
smoothed = ( 1/wl .* movsum(e,[wl/2,wl/2]) );
% remove interference
for level = 0:3
yk_lvsm.signal(pre_decision_level_uni==level) = yk_lvsm.signal(pre_decision_level_uni==level) - smoothed(pre_decision_level_uni==level);
yk_lvlp.signal(pre_decision_level_uni==level) = yk_lvlp.signal(pre_decision_level_uni==level) - filtered(pre_decision_level_uni==level);
end
%% PROCESS RX %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 1) Digi Demod
d_bm = digimod.demap(Eq_out);
d_dcsm = digimod.demap(yk_dcsm);
d_lvsm = digimod.demap(yk_lvsm);
d_lvlp = digimod.demap(yk_lvlp);
% 2) BER
dbit = length(d_bm.signal)-length(bitpattern);
[~,errors_bm,ber_bm(realiz),loc] = calc_ber(d_bm.signal(1:end-dbit,:) ,bitpattern(1:end,:),"skip",0,"returnErrorLocation",1);
[~,errors_dcsm,ber_dcsm(realiz)] = calc_ber(d_dcsm.signal(1:end-dbit,:) ,bitpattern(1:end,:),"skip",0,"returnErrorLocation",0);
[~,errors_lvsm,ber_lvsm(realiz)] = calc_ber(d_lvsm.signal(1:end-dbit,:) ,bitpattern(1:end,:),"skip",0,"returnErrorLocation",0);
[~,errors_lvlp,ber_lvlp(realiz)] = calc_ber(d_lvlp.signal(1:end-dbit,:) ,bitpattern(1:end,:),"skip",0,"returnErrorLocation",0);
end
output.ber_bm = mean(ber_bm);
output.ber_dcsm = mean(ber_dcsm);
output.ber_lvsm = mean(ber_lvsm);
output.ber_lvlp = mean(ber_lvlp);
end

416
run_loop.m → projects/IMDD_base_system/run_loop.m
View File

@@ -1,208 +1,208 @@
clear
sir_loop = [-25:2:-13];
sir_loop = 0;
lw_loop = [1,2,3];
data = cell(length(sir_loop),length(lw_loop));
iterations=size(data);
parfor ix = 1:numel(data)
[u1,u2] = ind2sub(iterations,ix);
output = imddmodel(sir_loop(u1),lw_loop(u2));
data{ix} = output;
end
for sir = 1:size(data,1)
for lw = 1:size(data,2)
ber_bm(sir,lw) = data{sir,lw}.ber_bm;
ber_dcsm(sir,lw) = data{sir,lw}.ber_dcsm;
ber_lvsm(sir,lw) = data{sir,lw}.ber_lvsm;
ber_lvlp(sir,lw) = data{sir,lw}.ber_lvlp;
end
end
col = [ 0.6510 0.8078 0.8902
0.6980 0.8745 0.5412
0.9922 0.7490 0.4353];
figure(22)
for l = 1:numel(lw_loop)
hold on
plot(sir_loop,ber_bm(:,l),'DisplayName',['DC mode:', num2str(lw_loop(l)),', Linewidth= 50 MHz'],'Marker','o','MarkerFaceColor',col(l,:),'Color',col(l,:),'LineWidth',2,'LineStyle','--');
end
set(gca,'yscale','log');
yline(3.8e-3,'LineWidth',2,'LineStyle','--','HandleVisibility','off');
set(gca,'xdir','reverse');
%% Plot
col = linspecer(8);
figure(1)
hold on
m = ["x","o","pentagram","hexagram","*","+"];
cnt = 1;
for d = 1%:size(data,2)
for dc = 1:size(data,1)
for lw = 1:size(data,3)
ber_bm(dc,d,lw) = (data{dc,d,lw}.ber_bm);
ber_dcsm(dc,d,lw) = data{dc,d,lw}.ber_dcsm;
ber_lvsm(dc,d,lw) = data{dc,d,lw}.ber_lvsm;
ber_lvlp(dc,d,lw) = (data{dc,d,lw}.ber_lvlp);
end
end
i = 1;
comm_dn = [];% ['EQ DC Tap: ',num2str(dc_tap_loop(d)),' m'];
title("Dependency on Laser Linewidth; B2B; Delay : 2*50m")
plot(lw_loop*1e-6,mean(squeeze(ber_bm(1:end,d,:)),1),"LineWidth",1.2,"Marker",m(1),"MarkerSize",5,'Color',col(cnt,:),'DisplayName',[' ',comm_dn]);
plot(lw_loop*1e-6,mean(squeeze(ber_dcsm(1:end,d,:)),1),"LineWidth",1,"Marker",m(1+4),"MarkerSize",5,'LineStyle','--','Color',col(cnt,:),'DisplayName',['DC smoothing ',comm_dn],'HandleVisibility','on');
plot(lw_loop*1e-6,mean(squeeze(ber_lvsm(1:end,d,:)),1),"LineWidth",1.2,"Marker",m(1+1),"MarkerSize",5,'LineStyle',':','Color',col(cnt,:),'DisplayName',['Lvl Smoothing ',comm_dn],'HandleVisibility','on');
plot(lw_loop*1e-6,mean(squeeze(ber_lvlp(1:end,d,:)),1),"LineWidth",1,"Marker",m(1+3),"MarkerSize",5,'LineStyle','-.','Color',col(cnt,:),'DisplayName',['Lvl Lowpass ',comm_dn],'HandleVisibility','on');
set(gca,'yscale','log');
set(gca,'xscale','log');
%xticklabels([10 100 1000 10000]);
grid minor
yline(3.8e-3,'LineWidth',2,'LineStyle','--','HandleVisibility','off');
ylim([1e-3 4e-2]);
ylabel("BER");
xlabel("Linewidth in MHz")
legend
cnt = cnt+1;
end
figure(1)
contour(lw_loop,sir_loop,thres_a0,16:0.4:21,'LineWidth',2,'FaceAlpha',0.3,'ShowText','on',"LabelFormat","%0.1f dB");
clim([16 21]);
ylabel("Bandwidth in Multiples of Linewidth");
xlabel("Linewidth in MHz");
% set(gca,'yscale','log');
grid minor
title("MPI removal - Optimization of Lowpass Filter Bandwidth")
figure(2)
contour(lw_loop,sir_loop([1:10,12:end]),thres_a0([1:10,12:end],:),16:0.3:21,'LineWidth',2,'FaceAlpha',0.3,'ShowText','on',"LabelFormat","%0.1f dB");
clim([16 21]);
ylabel("Window Length");
xlabel("Linewidth in MHz");
set(gca,'yscale','log');
grid minor
title("MPI removal - Optimization of Averaging Window Length")
%% Plot Winlen Contour
hdfec = 3.8e-3.*ones(size(sir_loop));
for dc = 1:size(data,2)
for lw = 1:size(data,3)
for s = 1:size(data,1)
ber_lvlp(s,dc,lw) = data{s,dc,lw}.ber_dcsm;
% ber_dcsm(wl,lw,s) = data{wl,s,lw}.ber_dcsm;
end
a_bm = InterX([sir_loop;squeeze(ber_lvlp(:,dc,lw))'],[sir_loop;hdfec]);
% a_dcsm = InterX([sirloop;squeeze(ber_dcsm(wl,lw,:))'],[sirloop;hdfec]);
try
thres_a0(dc,lw) = -a_bm(1);
%thres_a1(wl,lw) = -a_dcsm(1);
catch
thres_a0(dc,lw) = NaN;
%thres_a1(wl,lw) = NaN;
end
end
end
figure(1)
contour(lw_loop,bw_loop,thres_a0,14:0.2:21,'LineWidth',1.5,'FaceAlpha',0.3,'ShowText','on',"LabelFormat","%0.1f dB");
a = flip(cbrewer2('seq','Spectral',32));
a = [a(1:12,:); a(22:end,:)];
colormap(a);
clim([16 21]);
ylabel("Window Length");
xlabel("Linewidth in MHz");
yticks(bw_loop);
yticklabels(bw_loop);
set(gca,'yscale','log');
set(gca,'xscale','log');
grid minor
%% Plot DC Tap Contour
hdfec = 3.8e-3.*ones(size(bw_loop));
thres_a0 = zeros(size(ber,1),size(ber,3));
thres_a1 = zeros(size(ber,1),size(ber,3));
for dc = 1:size(ber,1)
for lw = 1:size(ber,3)
a_lvsm = InterX([bw_loop;ber(dc,:,lw)],[bw_loop;hdfec]);
thres_a0(dc,lw) = -a_lvsm(1);
a1 = InterX([bw_loop;ber_a1(dc,:,lw)],[bw_loop;hdfec]);
thres_a1(dc,lw) = -a1(1);
end
end
figure(1)
subplot(2,1,1)
contour(lw_loop,sir_loop,thres_a0,16:0.5:21,'LineWidth',3,'FaceAlpha',0.3,'ShowText','on',"LabelFormat","%0.1f dB");
clim([16 21]);
ylabel("DC Tap");
xlabel("Linewidth in MHz");
set(gca,'yscale','log');
grid minor
subplot(2,1,2)
contour(lw_loop,sir_loop,thres_a1,16:0.5:21,'LineWidth',3,'FaceAlpha',0.3,'ShowText','on',"LabelFormat","%0.1f dB");
clim([16 21]);
ylabel("DC Tap");
xlabel("Linewidth in MHz");
set(gca,'yscale','log');
grid minor
%% Plot Curves of required SIR to see the minimum a bit better
col = flip(cbrewer2('seq','Spectral',16));
col = col([1:4, 10:end],:);
figure(2)
hold on
for lw = [size(ber,3):-2:2 2 1]
plot(sir_loop,thres_a0(:,lw),'DisplayName',[' Linewidth: ',num2str(lw_loop(lw)*1e-6), ' MHz'],'Color',col(lw,:),'Marker','o','MarkerFaceColor',col(lw,:),'LineWidth',2);
set(gca,'xscale','log');
end
xlabel("DC Tap Value");
ylabel("Required SIR to rech FEC in dB");
%%
col = linspecer(7);
figure(3)
cnt=1;
for lw = [1,2,11]
for i = 1:length(sir_loop)-1
subplot(1,3,cnt)
hold on
plot(-1.*bw_loop,ber(i,:,lw),"LineWidth",2,"Marker","o","MarkerSize",5,'Color',col(i,:),'DisplayName',['DC tap ',num2str(sir_loop(i))]);
plot(-1.*bw_loop,ber_a1(i,:,lw),"LineWidth",2,"Marker","x","MarkerSize",5,'LineStyle','--','Color',col(i,:),'DisplayName',['A1. DC tap ',num2str(sir_loop(i))]);
yline(3.8e-3,'LineWidth',2,'LineStyle','--','HandleVisibility','off');
set(gca,'yscale','log');
grid minor
xlim([15,30]);
ylim([1e-4,1e-2]);
xlabel("SIR in dB");
ylabel("BER");
title(['BER for different SIR;',' Linewidth: ',num2str(lw_loop(lw)*1e-6), ' MHz'])
text(25,4.2e-3,"FEC $3.8 e^{-3}$");
end
cnt = cnt+1;
end
legend
clear
sir_loop = [-25:2:-13];
sir_loop = 0;
lw_loop = [1,2,3];
data = cell(length(sir_loop),length(lw_loop));
iterations=size(data);
parfor ix = 1:numel(data)
[u1,u2] = ind2sub(iterations,ix);
output = imddmodel(sir_loop(u1),lw_loop(u2));
data{ix} = output;
end
for sir = 1:size(data,1)
for lw = 1:size(data,2)
ber_bm(sir,lw) = data{sir,lw}.ber_bm;
ber_dcsm(sir,lw) = data{sir,lw}.ber_dcsm;
ber_lvsm(sir,lw) = data{sir,lw}.ber_lvsm;
ber_lvlp(sir,lw) = data{sir,lw}.ber_lvlp;
end
end
col = [ 0.6510 0.8078 0.8902
0.6980 0.8745 0.5412
0.9922 0.7490 0.4353];
figure(22)
for l = 1:numel(lw_loop)
hold on
plot(sir_loop,ber_bm(:,l),'DisplayName',['DC mode:', num2str(lw_loop(l)),', Linewidth= 50 MHz'],'Marker','o','MarkerFaceColor',col(l,:),'Color',col(l,:),'LineWidth',2,'LineStyle','--');
end
set(gca,'yscale','log');
yline(3.8e-3,'LineWidth',2,'LineStyle','--','HandleVisibility','off');
set(gca,'xdir','reverse');
%% Plot
col = linspecer(8);
figure(1)
hold on
m = ["x","o","pentagram","hexagram","*","+"];
cnt = 1;
for d = 1%:size(data,2)
for dc = 1:size(data,1)
for lw = 1:size(data,3)
ber_bm(dc,d,lw) = (data{dc,d,lw}.ber_bm);
ber_dcsm(dc,d,lw) = data{dc,d,lw}.ber_dcsm;
ber_lvsm(dc,d,lw) = data{dc,d,lw}.ber_lvsm;
ber_lvlp(dc,d,lw) = (data{dc,d,lw}.ber_lvlp);
end
end
i = 1;
comm_dn = [];% ['EQ DC Tap: ',num2str(dc_tap_loop(d)),' m'];
title("Dependency on Laser Linewidth; B2B; Delay : 2*50m")
plot(lw_loop*1e-6,mean(squeeze(ber_bm(1:end,d,:)),1),"LineWidth",1.2,"Marker",m(1),"MarkerSize",5,'Color',col(cnt,:),'DisplayName',[' ',comm_dn]);
plot(lw_loop*1e-6,mean(squeeze(ber_dcsm(1:end,d,:)),1),"LineWidth",1,"Marker",m(1+4),"MarkerSize",5,'LineStyle','--','Color',col(cnt,:),'DisplayName',['DC smoothing ',comm_dn],'HandleVisibility','on');
plot(lw_loop*1e-6,mean(squeeze(ber_lvsm(1:end,d,:)),1),"LineWidth",1.2,"Marker",m(1+1),"MarkerSize",5,'LineStyle',':','Color',col(cnt,:),'DisplayName',['Lvl Smoothing ',comm_dn],'HandleVisibility','on');
plot(lw_loop*1e-6,mean(squeeze(ber_lvlp(1:end,d,:)),1),"LineWidth",1,"Marker",m(1+3),"MarkerSize",5,'LineStyle','-.','Color',col(cnt,:),'DisplayName',['Lvl Lowpass ',comm_dn],'HandleVisibility','on');
set(gca,'yscale','log');
set(gca,'xscale','log');
%xticklabels([10 100 1000 10000]);
grid minor
yline(3.8e-3,'LineWidth',2,'LineStyle','--','HandleVisibility','off');
ylim([1e-3 4e-2]);
ylabel("BER");
xlabel("Linewidth in MHz")
legend
cnt = cnt+1;
end
figure(1)
contour(lw_loop,sir_loop,thres_a0,16:0.4:21,'LineWidth',2,'FaceAlpha',0.3,'ShowText','on',"LabelFormat","%0.1f dB");
clim([16 21]);
ylabel("Bandwidth in Multiples of Linewidth");
xlabel("Linewidth in MHz");
% set(gca,'yscale','log');
grid minor
title("MPI removal - Optimization of Lowpass Filter Bandwidth")
figure(2)
contour(lw_loop,sir_loop([1:10,12:end]),thres_a0([1:10,12:end],:),16:0.3:21,'LineWidth',2,'FaceAlpha',0.3,'ShowText','on',"LabelFormat","%0.1f dB");
clim([16 21]);
ylabel("Window Length");
xlabel("Linewidth in MHz");
set(gca,'yscale','log');
grid minor
title("MPI removal - Optimization of Averaging Window Length")
%% Plot Winlen Contour
hdfec = 3.8e-3.*ones(size(sir_loop));
for dc = 1:size(data,2)
for lw = 1:size(data,3)
for s = 1:size(data,1)
ber_lvlp(s,dc,lw) = data{s,dc,lw}.ber_dcsm;
% ber_dcsm(wl,lw,s) = data{wl,s,lw}.ber_dcsm;
end
a_bm = InterX([sir_loop;squeeze(ber_lvlp(:,dc,lw))'],[sir_loop;hdfec]);
% a_dcsm = InterX([sirloop;squeeze(ber_dcsm(wl,lw,:))'],[sirloop;hdfec]);
try
thres_a0(dc,lw) = -a_bm(1);
%thres_a1(wl,lw) = -a_dcsm(1);
catch
thres_a0(dc,lw) = NaN;
%thres_a1(wl,lw) = NaN;
end
end
end
figure(1)
contour(lw_loop,bw_loop,thres_a0,14:0.2:21,'LineWidth',1.5,'FaceAlpha',0.3,'ShowText','on',"LabelFormat","%0.1f dB");
a = flip(cbrewer2('seq','Spectral',32));
a = [a(1:12,:); a(22:end,:)];
colormap(a);
clim([16 21]);
ylabel("Window Length");
xlabel("Linewidth in MHz");
yticks(bw_loop);
yticklabels(bw_loop);
set(gca,'yscale','log');
set(gca,'xscale','log');
grid minor
%% Plot DC Tap Contour
hdfec = 3.8e-3.*ones(size(bw_loop));
thres_a0 = zeros(size(ber,1),size(ber,3));
thres_a1 = zeros(size(ber,1),size(ber,3));
for dc = 1:size(ber,1)
for lw = 1:size(ber,3)
a_lvsm = InterX([bw_loop;ber(dc,:,lw)],[bw_loop;hdfec]);
thres_a0(dc,lw) = -a_lvsm(1);
a1 = InterX([bw_loop;ber_a1(dc,:,lw)],[bw_loop;hdfec]);
thres_a1(dc,lw) = -a1(1);
end
end
figure(1)
subplot(2,1,1)
contour(lw_loop,sir_loop,thres_a0,16:0.5:21,'LineWidth',3,'FaceAlpha',0.3,'ShowText','on',"LabelFormat","%0.1f dB");
clim([16 21]);
ylabel("DC Tap");
xlabel("Linewidth in MHz");
set(gca,'yscale','log');
grid minor
subplot(2,1,2)
contour(lw_loop,sir_loop,thres_a1,16:0.5:21,'LineWidth',3,'FaceAlpha',0.3,'ShowText','on',"LabelFormat","%0.1f dB");
clim([16 21]);
ylabel("DC Tap");
xlabel("Linewidth in MHz");
set(gca,'yscale','log');
grid minor
%% Plot Curves of required SIR to see the minimum a bit better
col = flip(cbrewer2('seq','Spectral',16));
col = col([1:4, 10:end],:);
figure(2)
hold on
for lw = [size(ber,3):-2:2 2 1]
plot(sir_loop,thres_a0(:,lw),'DisplayName',[' Linewidth: ',num2str(lw_loop(lw)*1e-6), ' MHz'],'Color',col(lw,:),'Marker','o','MarkerFaceColor',col(lw,:),'LineWidth',2);
set(gca,'xscale','log');
end
xlabel("DC Tap Value");
ylabel("Required SIR to rech FEC in dB");
%%
col = linspecer(7);
figure(3)
cnt=1;
for lw = [1,2,11]
for i = 1:length(sir_loop)-1
subplot(1,3,cnt)
hold on
plot(-1.*bw_loop,ber(i,:,lw),"LineWidth",2,"Marker","o","MarkerSize",5,'Color',col(i,:),'DisplayName',['DC tap ',num2str(sir_loop(i))]);
plot(-1.*bw_loop,ber_a1(i,:,lw),"LineWidth",2,"Marker","x","MarkerSize",5,'LineStyle','--','Color',col(i,:),'DisplayName',['A1. DC tap ',num2str(sir_loop(i))]);
yline(3.8e-3,'LineWidth',2,'LineStyle','--','HandleVisibility','off');
set(gca,'yscale','log');
grid minor
xlim([15,30]);
ylim([1e-4,1e-2]);
xlabel("SIR in dB");
ylabel("BER");
title(['BER for different SIR;',' Linewidth: ',num2str(lw_loop(lw)*1e-6), ' MHz'])
text(25,4.2e-3,"FEC $3.8 e^{-3}$");
end
cnt = cnt+1;
end
legend

798
setup_simulation.m → projects/IMDD_base_system/setup_simulation.m
View File

@@ -1,399 +1,399 @@
lw = [0.1e6 1e6 10e6];
for lp = 1
rng(9);
%% A) Set Simulation Variables
sir = -18;
delay = 50; %mpi delay in meter
fiblen = 0; %main link in km
laser_linewidth =5e6;
O = 19; %order of prbs
N = 2^(O-1); %length of prbs
[~,seed] = prbs(O,1); %initialize first seed of prbs
% Modulation
M = 4; %PAM-M
bitpattern = zeros(N,log2(M));
% Symbol Rate
fsym = 112e9;
% DAC Rate
fdac = 120e9;
% Simulation oversampling rate "k";
kover = 16;
% ADC Rate
fadc = 256e9;
% Simulation frequency in "analog domain"
fsimu = kover * fdac ;
%% B) CONSTRUCT ALL CLASSES %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
digimod = PAMmapper(M,0);
pulseform = Pulseformer("pulseform","rrc","fdac",fdac,"fsym",fsym,"pulselength",32,"rrcalpha",0.027);
awg = AWG('fdac',fdac,'kover',kover,'lpf_active',1,'f_cutoff',56e9,'lpf_type',filtertypes.gaussian,'bit_resolution',5.5);
lp_laser = Filter('filtdegree',1,"f_cutoff",50e9,"fsamp",fdac*kover,"filterType",filtertypes.bessel_inp);
u_pi = 3.5;
vbias = (0.5*u_pi)-u_pi;
extmodlaser = EML("mode",eml_mode.im_cosinus,"power",5,"fsimu",fsimu,"lambda",1310,"bias",vbias,"u_pi",u_pi,"linewidth",laser_linewidth);
fib = Fiber("fsimu",fdac*kover,"fiber_length",fiblen,"alpha",0.2,"D",16,"lambda0",thz2nm(193.1),"gamma",0);
reflectionpoint = Amplifier("amp_mode","ideal_no_noise","gain_mode","gain","amplification_db",sir);
reflectionprop = Fiber("fsimu",fdac*kover,"fiber_length",delay/1000,"alpha",0.2,"D",16,"lambda0",1550,"gamma",0);
opticatten = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power","amplification_db",-8);
phdiode = Photodiode("fsimu",fdac*kover,"dark_current",2e-08,"responsivity",1,"temperature",20);
lp_diode = Filter('filtdegree',1,"f_cutoff",50e9,"fsamp",fdac*kover,"filterType",filtertypes.bessel_inp);
scp = Scope("fsimu",fdac*kover,"fadc",fadc,...
"delay",0,"fixed_delay",0,"lpf_bw",113e9,"filtertype",filtertypes.butterworth,...
"samplingdelay",0,"rand_samplingdelay",0,"freq_offset",0,"samp_jitter",0,...
"adcresolution",6,"quantbuffer",0.1,'block_dc',1);
eq = EQ("K",2,"plottrain",0,"plotfinal",0,...
"training_length",4096,"training_loops",2,...
"Ne",[50,5,3],"Nb",[3,0,0],...
"DCmu",0.05,"DDmu",[0.0004 0.0005 0.0006 0.0007 ],"DFEmu",0.005,"FFEmu",0.00,...
"dd_loops",2,"epsilon",[10 100 1000 ],"M",2,...
"thres",[0.005 0.004 0.0005 ],"l1act",0,"delay",0,"rho",0.0005,"ideal_dfe",0,"DB_aim",0);
eq = EQ_silas("Ne",[50,5,3],"Nb",[2,0,0],"trainlength",4096,...
"sps",2,...
"mu_dc_dd",0.05,...
"mu_dc_train",0.05,...
"mu_ffe_train",0,...
"mu_dfe_train",0.005,...
"mu_ffe_dd",[0.0004 0.0004 0.0004],...
"mu_dfe_dd",0.005,...
"ddloops",3,...
"trainloops",4,...
"dcmode",1);
%% C) PROCESS TX %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 1) PRBS Generation
for i = 1:log2(M)
[bitpattern(:,i),seed] = prbs(O,N,seed);
end
% 2 ) Build Inf. signal class
bits = Informationsignal(bitpattern);
% 3) Digi modulation -> PAM-M signal
digimod_out = digimod.map(bits);
% 4) Pulse shaping -> racos
X = pulseform.process(digimod_out);
% 5) AWG (lowpass, quantization, sample and hold)
X = awg.process(X);
% 6) Lowpass behavior of laser and hf-cable? why twice?
X = lp_laser.process(X);
% X = lp_laser.process(X);
% 7) Normalize signal
X = X.normalize("mode","oneone");
X.signal = X.signal .* 1.3800;
%% D) PROCESS OPTICAL CHANNEL %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 1) Laser; Modulation -> OPTICAL DOMAIN
[X,extmodlaser] = extmodlaser.process(X);
% 2) Main fiber propagation
X = fib.process(X);
if delay ~= 0
% 3) Reflection
% Reflection is just an attenuation
R = reflectionpoint.process(X);
% Propagate back and forth (actual fiber propagation)
R = reflectionprop.process(R);
% Delay the reflected signal
[R,n] = R.delay("delay_meter",delay);
% Add together
col = cbrewer2('qual','Paired',8);
xax = (1:X.length);% / fsimu * (physconst("LightSpeed")/1.4677);
xax_sec = (1:X.length) / fsimu .* 1e6;%* (physconst("LightSpeed")/1.4677);
xax_sec = xax_sec(n:end).';
thresh = pi/4;
phaseX = phase(X.signal(n:end));
phaseR = phase(R.signal(n:end));
phasediff = wrapToPi(phaseX-phaseR);
[pos_high] = find(abs(phasediff)>thresh);
[pos_low] = find(abs(phasediff)<=thresh);
figure(6);hold on;histogram(phaseX-phaseR,500,'EdgeAlpha',0)
figure()
subplot(3,1,1)
scatter(xax_sec.',wrapToPi(phaseX),4,'.','MarkerEdgeColor',col(2,:),'DisplayName','Desired Signal')
hold on
scatter(xax_sec,wrapToPi(phaseR),4,'.','MarkerEdgeColor',col(1,:),'DisplayName','Reflected Signal')
hold on
lg = legend;
lg.Location = "southwest";
xlim([xax_sec(1) xax_sec(end)]);
xlabel('time in $\mu$s')
ylim([-pi pi]);
yticks([-pi 0 pi])
yticklabels({'$-\pi$',0, '$\pi$'})
ylabel('$\phi$')
subplot(3,1,2)
scatter(xax_sec(pos_low),phasediff(pos_low),4,'.','MarkerEdgeColor',col(5,:),'DisplayName','Phase Difference')
hold on
scatter(xax_sec(pos_high),phasediff(pos_high),4,'.','MarkerEdgeColor',col(2,:),'HandleVisibility','off')
% yline(thresh,'LineWidth',2,'LineStyle','-','HandleVisibility','off')
% yline(-thresh,'LineWidth',2,'LineStyle','-','HandleVisibility','off')
lg = legend;
lg.Location = "southwest";
xlabel('time in $\mu$s')
xlim([xax_sec(1) xax_sec(end)]);
ylim([-pi pi]);
yticks([-pi 0 pi])
yticklabels({'$-\pi$',0, '$\pi$'})
ylabel('$\Delta \phi$')
X.signal = X.signal(n:end);
R.signal = R.signal(n:end);
X1 = X;
X = X+R;
subplot(3,1,3)
scatter(xax_sec(pos_low),abs(X.signal(pos_low).^2)*1000,4,'.','MarkerEdgeColor',col(5,:),'DisplayName','Constructive Interference');
hold on
scatter(xax_sec(pos_high),abs(X.signal(pos_high).^2)*1000,4,'.','MarkerEdgeColor',col(2,:),'DisplayName','Destructive Interference');
%scatter(xax_sec(pos_high),abs(X1.signal(pos_high).^2)*1000,4,'.','MarkerEdgeColor',col(1,:),'MarkerFaceAlpha',0.3,'DisplayName','Destructive Interference');
xlim([xax_sec(1) xax_sec(end)]);
xlabel('time in $\mu$s')
lg = legend;
lg.Location = "southwest";
ylabel('Optical power in mW');
disp(['SIR ',num2str(10*log10(X.power/R.power))]);
% cut reference signal to correct length (nessecary due to MPI delay)
digimod_out.signal = digimod_out.signal(round(n * fsym/fsimu) : end,:);
bitpattern = bitpattern(round(n * fsym/fsimu):end,:);
end
% plot(angle(R.signal))
% 4) Attenuation
X = opticatten.process(X);
% 5) Photo Diode -> ELECTRICAL DOMAIN
X = phdiode.process(X);
X = lp_diode.process(X);
%% E) PROCESS RX %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 1) Oscilloscope (Sampling to f_adc; Quantization; Bandwidth Limitation)
X = scp.process(X);
% 2) Resample to 2x symbol rate
X = X.resample("fs_out",2*fsym,"fs_in",fadc);
% 3) Normalize
Eq_in = X.normalize("mode","rms");
% 4) Equalize
% MPI reduction DC removal BEFORE EQ
wl = 3000; % symbols
Eq_in.signal = Eq_in.signal - 1/wl .* movsum( Eq_in.signal,[wl/2,wl/2]);
% Equalize Signal
[Eq_out] = eq.process(Eq_in,digimod_out);
%% A1: MPI reduction DC removal
wl = 1000; % symbols
yk_dcsm = Eq_out;
yk_dcsm.signal = Eq_out.signal - 1/wl .* movsum( Eq_out.signal,[wl/2,wl/2]);
%% A2: MPI reduction Level wise error removal
yk_lvsm = Eq_out;
yk_lvlp = Eq_out;
pre_decision_level_uni = digimod.decide_pamlevel(Eq_out);
pre_decision_level_bi = ( pre_decision_level_uni*2-3 ) .* 1/sqrt(5);
e = Eq_out.signal - pre_decision_level_bi;
lp_mpi = Filter('filtdegree',1,"f_cutoff",2e6,"fsamp",fsym,"filterType",filtertypes.bessel_inp);
filtered = lp_mpi.process(e);
wl = 30; % symbols
smoothed = ( 1/wl .* movsum(e,[wl/2,wl/2]) );
% remove interference
for level = 0:3
yk_lvsm.signal(pre_decision_level_uni==level) = yk_lvsm.signal(pre_decision_level_uni==level) - smoothed(pre_decision_level_uni==level);
yk_lvlp.signal(pre_decision_level_uni==level) = yk_lvlp.signal(pre_decision_level_uni==level) - filtered(pre_decision_level_uni==level);
end
% Calc EVM
evm_bm = calc_evm(Eq_out.signal, pre_decision_level_bi);
evm_dcsm = calc_evm(yk_dcsm.signal, pre_decision_level_bi);
evm_lsm = calc_evm(yk_lvsm.signal, pre_decision_level_bi);
evm_llp = calc_evm(yk_lvlp.signal, pre_decision_level_bi);
% figure(1);bar([evm_bm' evm_dcsm' evm_llp' evm_lsm']);ylim([0.01 0.1]);set(gca,'yscale','log');
%% PROCESS RX %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 1) Digi Demod
d_bm = digimod.demap(Eq_out);
d_dcsm = digimod.demap(yk_dcsm);
d_lvsm = digimod.demap(yk_lvsm);
d_lvlp = digimod.demap(yk_lvlp);
% 2) BER
dbit = length(d_bm.signal)-length(bitpattern);
[~,errors_bm,ber_bm,loc] = calc_ber(d_bm.signal(1:end-dbit,:) ,bitpattern(1:end,:),"skip",0,"returnErrorLocation",1);
[~,errors_dcsm,ber_dcsm] = calc_ber(d_dcsm.signal(1:end-dbit,:) ,bitpattern(1:end,:),"skip",0,"returnErrorLocation",0);
[~,errors_lvsm,ber_lvsm] = calc_ber(d_lvsm.signal(1:end-dbit,:) ,bitpattern(1:end,:),"skip",0,"returnErrorLocation",0);
[~,errors_lvlp,ber_lvlp] = calc_ber(d_lvlp.signal(1:end-dbit,:) ,bitpattern(1:end,:),"skip",0,"returnErrorLocation",0);
% Display BER
disp(['BER benchmark: ', sprintf('%2E',ber_bm), ' ERRORS: ' ,num2str(sum(errors_bm))]);
disp(['BER dc smooth (A1): ', sprintf('%2E',ber_dcsm), ' ERRORS: ' ,num2str(sum(errors_dcsm))]);
disp(['BER lv smooth (A2): ', sprintf('%2E',ber_lvsm), ' ERRORS: ' ,num2str(sum(errors_lvsm))]);
disp(['BER lv lowpas: ', sprintf('%2E',ber_lvlp), ' ERRORS: ' ,num2str(sum(errors_lvlp))]);
%% Generate some Plots
if 1
% SCATTER
col = cbrewer2('Paired',8);
xax = 1:Eq_out.length;
figure(3)
sgtitle('')
subplot(1,3,1)
hold on
eq_decision = digimod.decide_pamlevel(Eq_out);
true_symbols = digimod.decide_pamlevel(digimod_out);
xindices = 1:Eq_in.length;
errorpos = find(loc~=0)*2;
errorpos(errorpos>length(Eq_in.signal)) = length(Eq_in.signal);
correct = find(loc==0)*2;
correct(correct>length(Eq_in.signal)) = length(Eq_in.signal);
scatter(xindices(correct),Eq_in.signal(correct),4,'.','MarkerEdgeColor',col(4,:),'DisplayName','After EQ');
hold on
scatter(xindices(errorpos),Eq_in.signal(errorpos),6,'x','MarkerEdgeColor',col(6,:),'DisplayName','Wrong Decision');
hold off
xlim([1, xindices(end)]);
ylim([-3 3]);
xlabel('Sampling Index')
ylabel('Amplitude')
a = legend;
a.Location = "best";
subplot(1,3,2)
xindices = 1:Eq_out.length;
xax_sec = (1:Eq_out.length) / fsym .* 1e6;
scatter(xax_sec(loc==0),Eq_out.signal(loc==0),4,'.','MarkerEdgeColor',col(4,:),'DisplayName','After EQ');
hold on
scatter(xindices(loc~=0),Eq_out.signal(loc~=0),8,'x','MarkerEdgeColor',col(6,:),'DisplayName','Wrong Decision');
hold off
xlim([1, xax(end)]);
ylim([-2 2]);
xlabel('Sampling Index')
ylabel('Amplitude')
legend
a = legend;
a.Location = "best";
hold off
subplot(1,3,3)
xindices = 1:yk_lvsm.length;
scatter(xindices(loc==0),yk_lvsm.signal(loc==0),4,'.','MarkerEdgeColor',col(4,:),'DisplayName','After A2');
hold on
scatter(xindices(loc~=0),yk_lvsm.signal(loc~=0),8,'x','MarkerEdgeColor',col(6,:),'DisplayName','Wrong Decision');
hold off
xlim([1, xax(end)]);
ylim([-2 2]);
xlabel('Sampling Index')
ylabel('Amplitude')
legend
a = legend;
a.Location = "best";
hold off
end
if 0
col = cbrewer2('Paired',8);
figure(11)
clf
subplot(2,1,1)
hold on
plot(digimod_out.signal(4150:4175),'DisplayName','Tx','Color',col(1,:),'LineWidth',3);
plot(Eq_out.signal(4150:4175),'DisplayName','Rx after EQ','Color',col(6,:),'LineWidth',1);
title('Modulated Sequence Zoom');
legend
hold off
subplot(2,1,2)
hold on
stem(d_bm.signal(4150:4175,1),'DisplayName','Tx','Color',col(1,:),'LineStyle','-','LineWidth',5)
stem(bitpattern(4150:4175,1)','DisplayName','Rx','Color',col(6,:),'LineStyle','--','LineWidth',2)
title('Bitpattern Tx - Rx');
legend
hold off
end
end
lw = [0.1e6 1e6 10e6];
for lp = 1
rng(9);
%% A) Set Simulation Variables
sir = -18;
delay = 50; %mpi delay in meter
fiblen = 0; %main link in km
laser_linewidth =5e6;
O = 19; %order of prbs
N = 2^(O-1); %length of prbs
[~,seed] = prbs(O,1); %initialize first seed of prbs
% Modulation
M = 4; %PAM-M
bitpattern = zeros(N,log2(M));
% Symbol Rate
fsym = 112e9;
% DAC Rate
fdac = 120e9;
% Simulation oversampling rate "k";
kover = 16;
% ADC Rate
fadc = 256e9;
% Simulation frequency in "analog domain"
fsimu = kover * fdac ;
%% B) CONSTRUCT ALL CLASSES %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
digimod = PAMmapper(M,0);
pulseform = Pulseformer("pulseform","rrc","fdac",fdac,"fsym",fsym,"pulselength",32,"rrcalpha",0.027);
awg = AWG('fdac',fdac,'kover',kover,'lpf_active',1,'f_cutoff',56e9,'lpf_type',filtertypes.gaussian,'bit_resolution',5.5);
lp_laser = Filter('filtdegree',1,"f_cutoff",50e9,"fsamp",fdac*kover,"filterType",filtertypes.bessel_inp);
u_pi = 3.5;
vbias = (0.5*u_pi)-u_pi;
extmodlaser = EML("mode",eml_mode.im_cosinus,"power",5,"fsimu",fsimu,"lambda",1310,"bias",vbias,"u_pi",u_pi,"linewidth",laser_linewidth);
fib = Fiber("fsimu",fdac*kover,"fiber_length",fiblen,"alpha",0.2,"D",16,"lambda0",thz2nm(193.1),"gamma",0);
reflectionpoint = Amplifier("amp_mode","ideal_no_noise","gain_mode","gain","amplification_db",sir);
reflectionprop = Fiber("fsimu",fdac*kover,"fiber_length",delay/1000,"alpha",0.2,"D",16,"lambda0",1550,"gamma",0);
opticatten = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power","amplification_db",-8);
phdiode = Photodiode("fsimu",fdac*kover,"dark_current",2e-08,"responsivity",1,"temperature",20);
lp_diode = Filter('filtdegree',1,"f_cutoff",50e9,"fsamp",fdac*kover,"filterType",filtertypes.bessel_inp);
scp = Scope("fsimu",fdac*kover,"fadc",fadc,...
"delay",0,"fixed_delay",0,"lpf_bw",113e9,"filtertype",filtertypes.butterworth,...
"samplingdelay",0,"rand_samplingdelay",0,"freq_offset",0,"samp_jitter",0,...
"adcresolution",6,"quantbuffer",0.1,'block_dc',1);
eq = EQ("K",2,"plottrain",0,"plotfinal",0,...
"training_length",4096,"training_loops",2,...
"Ne",[50,5,3],"Nb",[3,0,0],...
"DCmu",0.05,"DDmu",[0.0004 0.0005 0.0006 0.0007 ],"DFEmu",0.005,"FFEmu",0.00,...
"dd_loops",2,"epsilon",[10 100 1000 ],"M",2,...
"thres",[0.005 0.004 0.0005 ],"l1act",0,"delay",0,"rho",0.0005,"ideal_dfe",0,"DB_aim",0);
eq = EQ_silas("Ne",[50,5,3],"Nb",[2,0,0],"trainlength",4096,...
"sps",2,...
"mu_dc_dd",0.05,...
"mu_dc_train",0.05,...
"mu_ffe_train",0,...
"mu_dfe_train",0.005,...
"mu_ffe_dd",[0.0004 0.0004 0.0004],...
"mu_dfe_dd",0.005,...
"ddloops",3,...
"trainloops",4,...
"dcmode",1);
%% C) PROCESS TX %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 1) PRBS Generation
for i = 1:log2(M)
[bitpattern(:,i),seed] = prbs(O,N,seed);
end
% 2 ) Build Inf. signal class
bits = Informationsignal(bitpattern);
% 3) Digi modulation -> PAM-M signal
digimod_out = digimod.map(bits);
% 4) Pulse shaping -> racos
X = pulseform.process(digimod_out);
% 5) AWG (lowpass, quantization, sample and hold)
X = awg.process(X);
% 6) Lowpass behavior of laser and hf-cable? why twice?
X = lp_laser.process(X);
% X = lp_laser.process(X);
% 7) Normalize signal
X = X.normalize("mode","oneone");
X.signal = X.signal .* 1.3800;
%% D) PROCESS OPTICAL CHANNEL %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 1) Laser; Modulation -> OPTICAL DOMAIN
[X,extmodlaser] = extmodlaser.process(X);
% 2) Main fiber propagation
X = fib.process(X);
if delay ~= 0
% 3) Reflection
% Reflection is just an attenuation
R = reflectionpoint.process(X);
% Propagate back and forth (actual fiber propagation)
R = reflectionprop.process(R);
% Delay the reflected signal
[R,n] = R.delay("delay_meter",delay);
% Add together
col = cbrewer2('qual','Paired',8);
xax = (1:X.length);% / fsimu * (physconst("LightSpeed")/1.4677);
xax_sec = (1:X.length) / fsimu .* 1e6;%* (physconst("LightSpeed")/1.4677);
xax_sec = xax_sec(n:end).';
thresh = pi/4;
phaseX = phase(X.signal(n:end));
phaseR = phase(R.signal(n:end));
phasediff = wrapToPi(phaseX-phaseR);
[pos_high] = find(abs(phasediff)>thresh);
[pos_low] = find(abs(phasediff)<=thresh);
figure(6);hold on;histogram(phaseX-phaseR,500,'EdgeAlpha',0)
figure()
subplot(3,1,1)
scatter(xax_sec.',wrapToPi(phaseX),4,'.','MarkerEdgeColor',col(2,:),'DisplayName','Desired Signal')
hold on
scatter(xax_sec,wrapToPi(phaseR),4,'.','MarkerEdgeColor',col(1,:),'DisplayName','Reflected Signal')
hold on
lg = legend;
lg.Location = "southwest";
xlim([xax_sec(1) xax_sec(end)]);
xlabel('time in $\mu$s')
ylim([-pi pi]);
yticks([-pi 0 pi])
yticklabels({'$-\pi$',0, '$\pi$'})
ylabel('$\phi$')
subplot(3,1,2)
scatter(xax_sec(pos_low),phasediff(pos_low),4,'.','MarkerEdgeColor',col(5,:),'DisplayName','Phase Difference')
hold on
scatter(xax_sec(pos_high),phasediff(pos_high),4,'.','MarkerEdgeColor',col(2,:),'HandleVisibility','off')
% yline(thresh,'LineWidth',2,'LineStyle','-','HandleVisibility','off')
% yline(-thresh,'LineWidth',2,'LineStyle','-','HandleVisibility','off')
lg = legend;
lg.Location = "southwest";
xlabel('time in $\mu$s')
xlim([xax_sec(1) xax_sec(end)]);
ylim([-pi pi]);
yticks([-pi 0 pi])
yticklabels({'$-\pi$',0, '$\pi$'})
ylabel('$\Delta \phi$')
X.signal = X.signal(n:end);
R.signal = R.signal(n:end);
X1 = X;
X = X+R;
subplot(3,1,3)
scatter(xax_sec(pos_low),abs(X.signal(pos_low).^2)*1000,4,'.','MarkerEdgeColor',col(5,:),'DisplayName','Constructive Interference');
hold on
scatter(xax_sec(pos_high),abs(X.signal(pos_high).^2)*1000,4,'.','MarkerEdgeColor',col(2,:),'DisplayName','Destructive Interference');
%scatter(xax_sec(pos_high),abs(X1.signal(pos_high).^2)*1000,4,'.','MarkerEdgeColor',col(1,:),'MarkerFaceAlpha',0.3,'DisplayName','Destructive Interference');
xlim([xax_sec(1) xax_sec(end)]);
xlabel('time in $\mu$s')
lg = legend;
lg.Location = "southwest";
ylabel('Optical power in mW');
disp(['SIR ',num2str(10*log10(X.power/R.power))]);
% cut reference signal to correct length (nessecary due to MPI delay)
digimod_out.signal = digimod_out.signal(round(n * fsym/fsimu) : end,:);
bitpattern = bitpattern(round(n * fsym/fsimu):end,:);
end
% plot(angle(R.signal))
% 4) Attenuation
X = opticatten.process(X);
% 5) Photo Diode -> ELECTRICAL DOMAIN
X = phdiode.process(X);
X = lp_diode.process(X);
%% E) PROCESS RX %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 1) Oscilloscope (Sampling to f_adc; Quantization; Bandwidth Limitation)
X = scp.process(X);
% 2) Resample to 2x symbol rate
X = X.resample("fs_out",2*fsym,"fs_in",fadc);
% 3) Normalize
Eq_in = X.normalize("mode","rms");
% 4) Equalize
% MPI reduction DC removal BEFORE EQ
wl = 3000; % symbols
Eq_in.signal = Eq_in.signal - 1/wl .* movsum( Eq_in.signal,[wl/2,wl/2]);
% Equalize Signal
[Eq_out] = eq.process(Eq_in,digimod_out);
%% A1: MPI reduction DC removal
wl = 1000; % symbols
yk_dcsm = Eq_out;
yk_dcsm.signal = Eq_out.signal - 1/wl .* movsum( Eq_out.signal,[wl/2,wl/2]);
%% A2: MPI reduction Level wise error removal
yk_lvsm = Eq_out;
yk_lvlp = Eq_out;
pre_decision_level_uni = digimod.decide_pamlevel(Eq_out);
pre_decision_level_bi = ( pre_decision_level_uni*2-3 ) .* 1/sqrt(5);
e = Eq_out.signal - pre_decision_level_bi;
lp_mpi = Filter('filtdegree',1,"f_cutoff",2e6,"fsamp",fsym,"filterType",filtertypes.bessel_inp);
filtered = lp_mpi.process(e);
wl = 30; % symbols
smoothed = ( 1/wl .* movsum(e,[wl/2,wl/2]) );
% remove interference
for level = 0:3
yk_lvsm.signal(pre_decision_level_uni==level) = yk_lvsm.signal(pre_decision_level_uni==level) - smoothed(pre_decision_level_uni==level);
yk_lvlp.signal(pre_decision_level_uni==level) = yk_lvlp.signal(pre_decision_level_uni==level) - filtered(pre_decision_level_uni==level);
end
% Calc EVM
evm_bm = calc_evm(Eq_out.signal, pre_decision_level_bi);
evm_dcsm = calc_evm(yk_dcsm.signal, pre_decision_level_bi);
evm_lsm = calc_evm(yk_lvsm.signal, pre_decision_level_bi);
evm_llp = calc_evm(yk_lvlp.signal, pre_decision_level_bi);
% figure(1);bar([evm_bm' evm_dcsm' evm_llp' evm_lsm']);ylim([0.01 0.1]);set(gca,'yscale','log');
%% PROCESS RX %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 1) Digi Demod
d_bm = digimod.demap(Eq_out);
d_dcsm = digimod.demap(yk_dcsm);
d_lvsm = digimod.demap(yk_lvsm);
d_lvlp = digimod.demap(yk_lvlp);
% 2) BER
dbit = length(d_bm.signal)-length(bitpattern);
[~,errors_bm,ber_bm,loc] = calc_ber(d_bm.signal(1:end-dbit,:) ,bitpattern(1:end,:),"skip",0,"returnErrorLocation",1);
[~,errors_dcsm,ber_dcsm] = calc_ber(d_dcsm.signal(1:end-dbit,:) ,bitpattern(1:end,:),"skip",0,"returnErrorLocation",0);
[~,errors_lvsm,ber_lvsm] = calc_ber(d_lvsm.signal(1:end-dbit,:) ,bitpattern(1:end,:),"skip",0,"returnErrorLocation",0);
[~,errors_lvlp,ber_lvlp] = calc_ber(d_lvlp.signal(1:end-dbit,:) ,bitpattern(1:end,:),"skip",0,"returnErrorLocation",0);
% Display BER
disp(['BER benchmark: ', sprintf('%2E',ber_bm), ' ERRORS: ' ,num2str(sum(errors_bm))]);
disp(['BER dc smooth (A1): ', sprintf('%2E',ber_dcsm), ' ERRORS: ' ,num2str(sum(errors_dcsm))]);
disp(['BER lv smooth (A2): ', sprintf('%2E',ber_lvsm), ' ERRORS: ' ,num2str(sum(errors_lvsm))]);
disp(['BER lv lowpas: ', sprintf('%2E',ber_lvlp), ' ERRORS: ' ,num2str(sum(errors_lvlp))]);
%% Generate some Plots
if 1
% SCATTER
col = cbrewer2('Paired',8);
xax = 1:Eq_out.length;
figure(3)
sgtitle('')
subplot(1,3,1)
hold on
eq_decision = digimod.decide_pamlevel(Eq_out);
true_symbols = digimod.decide_pamlevel(digimod_out);
xindices = 1:Eq_in.length;
errorpos = find(loc~=0)*2;
errorpos(errorpos>length(Eq_in.signal)) = length(Eq_in.signal);
correct = find(loc==0)*2;
correct(correct>length(Eq_in.signal)) = length(Eq_in.signal);
scatter(xindices(correct),Eq_in.signal(correct),4,'.','MarkerEdgeColor',col(4,:),'DisplayName','After EQ');
hold on
scatter(xindices(errorpos),Eq_in.signal(errorpos),6,'x','MarkerEdgeColor',col(6,:),'DisplayName','Wrong Decision');
hold off
xlim([1, xindices(end)]);
ylim([-3 3]);
xlabel('Sampling Index')
ylabel('Amplitude')
a = legend;
a.Location = "best";
subplot(1,3,2)
xindices = 1:Eq_out.length;
xax_sec = (1:Eq_out.length) / fsym .* 1e6;
scatter(xax_sec(loc==0),Eq_out.signal(loc==0),4,'.','MarkerEdgeColor',col(4,:),'DisplayName','After EQ');
hold on
scatter(xindices(loc~=0),Eq_out.signal(loc~=0),8,'x','MarkerEdgeColor',col(6,:),'DisplayName','Wrong Decision');
hold off
xlim([1, xax(end)]);
ylim([-2 2]);
xlabel('Sampling Index')
ylabel('Amplitude')
legend
a = legend;
a.Location = "best";
hold off
subplot(1,3,3)
xindices = 1:yk_lvsm.length;
scatter(xindices(loc==0),yk_lvsm.signal(loc==0),4,'.','MarkerEdgeColor',col(4,:),'DisplayName','After A2');
hold on
scatter(xindices(loc~=0),yk_lvsm.signal(loc~=0),8,'x','MarkerEdgeColor',col(6,:),'DisplayName','Wrong Decision');
hold off
xlim([1, xax(end)]);
ylim([-2 2]);
xlabel('Sampling Index')
ylabel('Amplitude')
legend
a = legend;
a.Location = "best";
hold off
end
if 0
col = cbrewer2('Paired',8);
figure(11)
clf
subplot(2,1,1)
hold on
plot(digimod_out.signal(4150:4175),'DisplayName','Tx','Color',col(1,:),'LineWidth',3);
plot(Eq_out.signal(4150:4175),'DisplayName','Rx after EQ','Color',col(6,:),'LineWidth',1);
title('Modulated Sequence Zoom');
legend
hold off
subplot(2,1,2)
hold on
stem(d_bm.signal(4150:4175,1),'DisplayName','Tx','Color',col(1,:),'LineStyle','-','LineWidth',5)
stem(bitpattern(4150:4175,1)','DisplayName','Rx','Color',col(6,:),'LineStyle','--','LineWidth',2)
title('Bitpattern Tx - Rx');
legend
hold off
end
end

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%% Settings
clear
filename = '112G_2';
load_sequence = 0;
M = 4;
datarate = 112e9;
kover = 8;
fsym = round(datarate*1e-9 / log2(M))*1e9;
%fsym = 50e9;
fdac = 256e9;
fadc = 256e9;
laser_linewidth = 1e6;
mpi_ = 50; %meter
sir = 20; %decibel = attenuation of interference path
lowpass_cutoff = fsym/2 * 1;
awg_bw = lowpass_cutoff;
mod_bw = lowpass_cutoff;
phd_bw = lowpass_cutoff;
scp_bw = lowpass_cutoff;
mpi_path=50;
vp = [0.25];
vb = [0.5:0.05:0.8];
vb = 0.7;
pn_key = [27];
rop = -5;
LP_awg = Filter('filtdegree',2,"f_cutoff",awg_bw,"fs",fdac,"filterType","butterworth");
LP_laser = Filter('filtdegree',4,"f_cutoff",mod_bw,"fs",fdac*kover,"filterType",filtertypes.gaussian);
LP_opt = Filter('filtdegree',4,"f_cutoff",fsym/log2(M).*1.5,"fs",fdac*kover,"filterType",filtertypes.gaussian);
LP_phd = Filter('filtdegree',2,"f_cutoff",phd_bw,"fs",fdac*kover,"filterType",filtertypes.butterworth);
LP_scpe = Filter('filtdegree',2,"f_cutoff",scp_bw,"fs",fadc,"filterType","butterworth");
for pnk = 1:length(pn_key)
for m = 1:length(vb)
if load_sequence
load(['C:\Users\Silas\Documents\MATLAB\imdd_simulation\projects\MPI_April\',filename,'.mat'],'X');
spectrum_plot(X.signal',X.fs,'spectrum');
else
% 1) PRBS Generation
O = 17; %order of prbs
N = 2^(O-1); %length of prbs
[~,seed] = prbs(O,1); %initialize first seed of prbs
bitpattern=[];
for i = 1:log2(M)
[bitpattern(:,i),seed] = prbs(O,N,seed);
end
% 2 ) Build Inf. signal class
bits = Informationsignal(bitpattern);
% 3) Digi modulation -> PAM-M signal
digimod_out = PAMmapper(M,0).map(bits);
digimod_out.fs = fsym;
X = Pulseformer("fsym",fsym,"fdac",fdac,"pulse","rrc","pulselength",16,"rrcalpha",0.01).process(digimod_out);
% 5) AWG (lowpass, quantization, sample and hold)
%X = M8196A().process(X);
kover = 16;
X = AWG("fdac",fdac,"dac_min",-1,"dac_max",1,"H_lpf",LP_awg,"kover",kover).process(X);
% 6) Lowpass behavior before laser
X = LP_laser.process(X);
% 7) Normalize signal
X = X.normalize("mode","oneone");
%save(['C:\Users\Silas\Documents\MATLAB\imdd_simulation\projects\MPI_April\',char(filename)],'X');
end
% 1) Laser; Modulation -> OPTICAL DOMAIN
u_pi = 2;
vbias = -vb(m)*u_pi;
extmodlaser = EML("mode",eml_mode.im_cosinus,"power",3,"fsimu",X.fs,"lambda",1290,"bias",vbias,"u_pi",u_pi,"linewidth",laser_linewidth,"randomkey",pn_key(pnk));
E = X.*vp(1);
[Opt,extmodlaser] = extmodlaser.process(E);
% figure(m)
% hold on
% scatter(E.signal(1:100000),(abs(Opt.signal(1:100000)).^2)*1e3,0.1,'.','DisplayName','Modulator TF')
% xlabel('Input in V')
% ylabel('abs(Output) in mW')
Opt = LP_opt.process(Opt);
cspr(m) = Opt.cspr;
% 2) ping pong fiber propagation
Interference_sig = Fiber("fsimu",Opt.fs,"fiber_length",mpi_path*2/1000,"alpha",0,"D",0,"lambda0",1310,"gamma",0).process(Opt);
Interference_sig = Amplifier("amp_mode","ideal_no_noise","gain_mode","gain","amplification_db",-sir).process(Interference_sig);
% In the meantime: delay the main signal
[Main_sig,n] = Opt.delay("delay_meter",mpi_path*2);
% Add
Combined_sig = Main_sig + Interference_sig;
% Cut (due to the delays there is a jump in the signals)
if n == 0;n = 1;end
Combined_sig.signal = Combined_sig.signal(ceil(n):end);
% Fiber
Combined_sig = Fiber("fsimu",Combined_sig.fs,"fiber_length",0,"alpha",0.3,"D",0,"lambda0",1310,"gamma",0,"Dslope",0.08).process(Combined_sig);
for i = 1:length(rop)
% Set ROP
Rx_sig = Amplifier("amp_mode","ideal_no_noise","gain_mode","gain","amplification_db",rop(i)).process(Combined_sig);
rop_save(m,i) = Rx_sig.power;
% Square Law
Rx_sig = Photodiode("fsimu",Rx_sig.fs,"dark_current",2e-08,"responsivity",1,"temperature",20).process(Rx_sig);
%Lowpass PhDiode
Rx_sig = LP_phd.process(Rx_sig);
% Scope
Rx_sig = Scope("fsimu",Rx_sig.fs,"fadc",fadc,...
"delay",0,"fixed_delay",0,"lpf_bw",scp_bw,"filtertype",filtertypes.butterworth,...
"samplingdelay",0,"rand_samplingdelay",0,"freq_offset",0,"samp_jitter",0,...
"adcresolution",16,"quantbuffer",0.1,'block_dc',1,'lpf_active',1,'H_lpf',LP_scpe).process(Rx_sig);
% Sample to 2x fsym
Rx_sig = Rx_sig.resample("fs_in",fadc,"fs_out",2*fsym);
% Sync Rx signal with reference
[Rx_sig,D,cuts] = Rx_sig.tsynch("reference",digimod_out,"fs_ref",fsym);
% % simple EQ (optimum mudc: 0.05 -> 0.005)
EQ_sig = EQ_silas_plain("Ne",[20,8,8],"Nb",[2,0,0],"trainlength",4096,"mu_dc_dd",0.005,"mu_dc_train",0.05,...
"mu_ffe_train",0.005,"mu_combined_dd",[0.0004 0.0006 0.0003 0.005],"ddloops",3,'trainloops',3,'sps',2).process(Rx_sig,digimod_out);
% Demap
Rx_Bits = PAMmapper(M,0).demap(EQ_sig);
Rx_symboldecision = PAMmapper(M,0).decide_pamlevel(EQ_sig,"symbol_levels",unique(digimod_out.signal));
levels = PAMmapper(M,0).separate_pamlevels(EQ_sig);
level_avg(:,m,i) = mean(levels,'omitnan');
level_std(:,m,i) = std(levels,'omitnan');
% BER
[~,errors_bm,BER(m,i),errors] = calc_ber(Rx_Bits.signal,bitpattern,"skip_front",0,"skip_end",0,"returnErrorLocation",1);
formatted_ber = sprintf('%.1e', BER(m,i));
disp(formatted_ber);
end
end
end
plot_analysis_window;
save(['C:\Users\Silas\Nextcloud4\Dokumente\02_Ablage_Office\MPI\Investigation_April_2024\','PAM_',num2str(M),'_mpi_',num2str(mpi_path),'_lw_',num2str(laser_linewidth)],"BER");
% BER plot
figure(340)
cols = linspecer(7);
for m = 1:size(BER,1)
hold on
plot(rop,BER(m,:),'DisplayName',['Bias: ',num2str(vb(m)), ' V'],'LineStyle','-','Color',cols(m,:),'LineWidth',1,'Marker','o','MarkerEdgeColor',[1 1 1],'MarkerFaceColor',cols(m,:));
end
set(gca,'YScale','log');
legend
xlabel("ROP in dBm")
yline(3.8e-3,'DisplayName','FEC');
figure(21)
hold on
plot(rop,mean(BER),'DisplayName',['Modulation: ',num2str(2*vp/extmodlaser.u_pi*100), ' $\%$'],'LineStyle','-','Color',cols(2,:),'LineWidth',1);
set(gca,'YScale','log');
legend
ylabel("ROP in dBm")
yline(3.8e-3,'DisplayName','FEC');
%check Rx and TX symbols
figure(101)
scatter(1:100,Rx_symboldecision.signal(1:100),10,'o');
hold on
scatter(1:100,digimod_out.signal(1:100),5,'x');

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%straight outta workspace, show everything I want
cols = linspecer(8);
colpairs = cbrewer2('paired',8);
f=figure(20);
clf(f)
f.Name="Optspec";
%Spectrum of Optical Signal after superposition
subplot(3,2,1:2)
spectrum_plot(Combined_sig.normalize("mode","rms").signal',Combined_sig.fs,'Optspec',['Papr: ',num2str(Combined_sig.papr), '; CSPR: ',num2str(Combined_sig.cspr)]);
hold on
ylim([-100,0]);
%Phase Investigation
subplot(3,2,3)
hold on
phase_int = extmodlaser.phase(n:end);
phase_main = delayseq(extmodlaser.phase,n);
phase_main = phase_main(n:end);
phase_diff = phase_int - phase_main;
t = (1:length(phase_int))' ./ extmodlaser.fsimu ;
plot(t*1e6,phase_int,'Color',colpairs(1,:),'DisplayName','Interferer Phase');
plot(t*1e6,phase_main,'Color',colpairs(2,:),'DisplayName','Signal Phase (delayed)');
plot(t*1e6,phase_diff,'Color',colpairs(4,:),'DisplayName','Delta Phase');
legend
subplot(3,2,4)
%Received Signal after Phdiode
yyaxis left
t = (1:Rx_sig.length)' ./ Rx_sig.fs;
scatter(t*1e6,Rx_sig.normalize("mode","oneone").signal.*max(unique(digimod_out.signal)),1,'.','MarkerEdgeColor',cols(5,:));
hold on
errors_t = errors./Rx_Bits.fs;
errors_sym = Rx_symboldecision.signal(errors);
scatter(errors_t*1e6,errors_sym,2,'x','LineWidth',1);
yline(PAMmapper(M,0).thresholds);
%Again: Phase Diff
yyaxis right
t = (1:length(phase_int))' ./ extmodlaser.fsimu ;
plot(t*1e6,phase_diff,'Color',colpairs(4,:),'DisplayName','Delta Phase');
title(['Lwidth:',num2str(laser_linewidth) ,'; SIR: ', num2str(sir) ,'dB; ROP: ',num2str(rop(i)),' dBm'])
xlabel('t in $\mu$s')
legend
%Equalized Signal
subplot(3,2,5)
t = (1:EQ_sig.length)' ./ EQ_sig.fs;
for h = 1:size(levels,2)
scatter(t*1e6,levels(:,h),1,'.');
hold on
end
yline(PAMmapper(M,0).thresholds);
title(['BER: ',sprintf('%.1e', BER(m,i))]);
%Histogram of EQzed Signal
subplot(3,2,6)
for h = 1:size(levels,2)
std_dev = std(levels(:,h),'omitnan');
mean_val = mean(levels(:,h),'omitnan');
hold on
histogram(levels(:,h),1000,"EdgeColor","none","Normalization","pdf");
text(mean_val, 0.1, ['$\sigma^2:$ ',sprintf('%.2f', std_dev)], 'HorizontalAlignment', 'center');
end
hold on
xline(PAMmapper(M,0).thresholds);
xticks(sort([unique(digimod_out.signal);PAMmapper(M,0).thresholds']));

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function simulate_tx_sig(filename)
%% Params
M=4;
digimod = PAMmapper(M,0);
fdac = 120e9;
fsym = 112e9;
pulseform = Pulseformer("pulseform","rrc","fdac",fdac,"fsym",fsym,"pulselength",32,"rrcalpha",0.027);
kover = 16;
awg = AWG('fdac',fdac,'kover',kover,'lpf_active',1,'f_cutoff',56e9,'lpf_type',filtertypes.gaussian,'bit_resolution',5.5);
%awg = M8199B();
lp_laser = Filter('filtdegree',2,"f_cutoff",50e9,"fsamp",fdac*kover,"filterType",filtertypes.butterworth);
%% PROCESS TX
% 1) PRBS Generation
O = 20; %order of prbs
N = 2^(O-1); %length of prbs
[~,seed] = prbs(O,1); %initialize first seed of prbs
for i = 1:log2(M)
[bitpattern(:,i),seed] = prbs(O,N,seed);
end
% 2 ) Build Inf. signal class
bits = Informationsignal(bitpattern);
% 3) Digi modulation -> PAM-M signal
digimod_out = digimod.map(bits);
% 4) Pulse shaping -> racos
X = pulseform.process(digimod_out);
% 5) AWG (lowpass, quantization, sample and hold)
X = awg.process(X);
% 6) Lowpass behavior before laser
X = lp_laser.process(X);
% 7) Normalize signal
X = X.normalize("mode","oneone");
X.signal = X.signal;
spectrum_plot(X.signal',X.fs,'spectrum');
if nargin == 1
save(['C:\Users\Silas\Documents\MATLAB\imdd_simulation\projects\',char(filename)],'X');
end
end

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clear;
col = linspecer(6);
% GENERATE SIR CURVE
if ismac
foldername = '/Users/silasoettinghaus/Documents/MATLAB/Labor_Datensatz_PAM4_MPI/pam4_10km_1km';
else
foldername = 'C:\Users\Silas\Documents\MATLAB\Datensätze\Labor_Datensatz_PAM4_MPI_OFC2023\pam4_10km_1km';
end
% SETTINGS
optimize_mudc = 0;
run_sir_sweep = 0;
run_feed_forward = 1;
run_baseline = 0;
run_ideal_dc_tap = 0;
plot_timesignal = 1;
block_loop = [1];
for bl = 1:numel(block_loop)
eq_parallelization_blocklength = block_loop(bl);
eq_updatelatency = 3;
eq_avg_blocklength =0;
% FIND BEST MU DC
if optimize_mudc
mudc_loop = [0, 0.0001,0.0005, 0.001,0.005, 0.01:0.01:0.1, 0.2:0.1:1];
current_filename = 'pam4__loop_14_92Gbd_19092023_1513.mat';
current_filename = 'pam4__loop_29_56Gbd_19092023_1546.mat';
recorded_data = load([foldername,filesep, current_filename]);
[best_mudc,ber]= optimizeMuDc(recorded_data,eq_parallelization_blocklength,eq_updatelatency,eq_avg_blocklength,mudc_loop);
% figure(16)
% hold on
% plot(mudc_loop,ber);
% set(gca, 'YScale', 'log');
% set(gca, 'XScale', 'log');
% scatter(best_mudc,ber(mudc_loop==best_mudc),100,'Marker','x','LineWidth',2)
% yline(ber(1));
% xlim([0,1])
end
if run_sir_sweep
mudc = best_mudc;
[ber,sir,fsym] = runSIRsweep(eq_parallelization_blocklength,eq_updatelatency,eq_avg_blocklength,mudc);
plotSirSweep(ber,sir,fsym,eq_parallelization_blocklength,eq_updatelatency,eq_avg_blocklength,mudc)
crossing = calculateCrossing([92e9, 56e9],ber,sir,fsym);
req_sir_56(bl) = crossing(2,2) ;
req_sir_92(bl) = crossing(2,1) ;
end
end
if run_feed_forward
current_filename = 'pam4__loop_14_92Gbd_v19092023_1513.mat';
recorded_data = load([foldername,filesep, current_filename]);
eq_avg_blocklength = [50,100,1000,3000];
[best_block,ber]= optimizeAvgBlocklength(recorded_data,eq_parallelization_blocklength,eq_updatelatency,eq_avg_blocklength,0);
best_block = 400;
[ber_base,sir_base,fsym_base] = runSIRsweep(eq_parallelization_blocklength,eq_updatelatency,best_block,0);
plotSirSweep(ber_base,sir_base,fsym_base,eq_parallelization_blocklength,eq_updatelatency,best_block,0)
crossing = calculateCrossing([92e9, 56e9],ber_base,sir_base,fsym_base);
[ber_base,sir_base,fsym_base] = runSIRsweep(eq_parallelization_blocklength,eq_updatelatency,0,0);
plotSirSweep(ber_base,sir_base,fsym_base,eq_parallelization_blocklength,eq_updatelatency,0,0)
crossing = calculateCrossing([92e9, 56e9],ber_base,sir_base,fsym_base);
end
if run_baseline
mudc = 0;
[ber_base,sir_base,fsym_base] = runSIRsweep(eq_parallelization_blocklength,eq_updatelatency,eq_avg_blocklength,mudc);
plotSirSweep(ber_base,sir_base,fsym_base,eq_parallelization_blocklength,eq_updatelatency,eq_avg_blocklength,mudc)
crossing = calculateCrossing([92e9, 56e9],ber_base,sir_base,fsym_base);
req_sir_56_baseline = crossing(2,2) ;
req_sir_92_baseline = crossing(2,1) ;
end
if run_ideal_dc_tap
current_filename = 'pam4__loop_29_56Gbd_19092023_1546.mat';
recorded_data = load([foldername,filesep, current_filename]);
[mudc,~]= optimizeMuDc(recorded_data,eq_parallelization_blocklength,eq_updatelatency,eq_avg_blocklength,mudc_loop);
eq_parallelization_blocklength = 1;
eq_updatelatency = 1;
[ber_ideal,sir_ideal,fsym_ideal] = runSIRsweep(eq_parallelization_blocklength,eq_updatelatency,eq_avg_blocklength,mudc);
plotSirSweep(ber_ideal,sir_ideal,fsym_ideal,eq_parallelization_blocklength,eq_updatelatency,eq_avg_blocklength,mudc)
crossing = calculateCrossing([92e9, 56e9],ber_ideal,sir_ideal,fsym_ideal);
req_sir_56_ideal = crossing(2,2) ;
req_sir_92_ideal = crossing(2,1) ;
end
if plot_timesignal
current_filename = 'pam4__loop_26_92Gbd_19092023_1542.mat';
recorded_data = load([foldername,filesep, current_filename]);
sir = recorded_data.saveStructTemp.awg2scope_keysight_state.eigenlight_mpi - 3.3 + 7.2;
fsym = 92e9;
eq_parallelization_blocklength = 1;
eq_updatelatency = 1;
eq_avg_blocklength = 0;
% TRUE SYMBOLS
correct_symbols = recorded_data.saveStructTemp.digi_mod_out';
%3) PLAIN RECEIVED SIGNAL
disp('RX Signal')
y_rx = Electricalsignal(recorded_data.saveStructTemp.dp_tsynch_out');
y_rx.fs = 2.*fsym;
rx_symbols = y_rx.resample("fs_in",2.*fsym,"fs_out",fsym);
rx_symbols = rx_symbols.normalize("mode","rms").signal;
disp(std(rx_symbols));
scatterleveldependent(rx_symbols,correct_symbols,fsym);
%1) PLOT WITH WITH ALGORITHM
if 0
mudc_loop = [0, 0.0001,0.0005, 0.001,0.005, 0.01:0.01:0.1, 0.2:0.1:1];
[mudc,~]= optimizeMuDc(recorded_data,eq_parallelization_blocklength,eq_updatelatency,eq_avg_blocklength,mudc_loop);
end
disp('ALGORITHM ')
mudc = 0.05;
results_alg = runPostProcessing(recorded_data,mudc,eq_parallelization_blocklength,eq_updatelatency,eq_avg_blocklength);
disp(results_alg.ber);
rx_symbols = results_alg.EQ_out.signal;
disp(std(rx_symbols));
scatterleveldependent(rx_symbols,correct_symbols,fsym);
%2) JUST EQ
disp('Just EQ')
mudc = 0;
results = runPostProcessing(recorded_data,mudc,eq_parallelization_blocklength,eq_updatelatency,eq_avg_blocklength);
disp(results.ber)
rx_symbols = results.EQ_out.signal;
disp(std(rx_symbols));
scatterleveldependent(rx_symbols,correct_symbols,fsym);
end
function plotSirSweep(ber,sir,fsym,eq_parallelization_blocklength,eq_updatelatency,eq_avg_blocklength,mudc)
rate = [92e9, 56e9];
figure(95)
for r = 1:length(rate)
sorted = sortrows([sir(fsym == rate(r)); ber(fsym == rate(r))]',1)';
hold on
plot(abs(sorted(1,:)),sorted(2,:),'DisplayName',[num2str(rate(r).*1e-9),' GBd; PAM4; mudc: ',num2str(mudc)],'LineWidth',1,'Marker','o','MarkerSize',5,'LineStyle','-','HandleVisibility','on');
end
set(gca, 'YScale', 'log');
yline(3.8e-3,'LineWidth',1, 'LineStyle','--','HandleVisibility','off');
xlim([15,35]);
xlabel('SIR in dB');
ylabel('BER');
end
function [crossing] = calculateCrossing(rate,ber,sir,fsym)
for r = 1:length(rate)
sorted = sortrows([sir(fsym == rate(r)); ber(fsym == rate(r))]',1)';
berVals = sorted(2,:);
xAxis = sorted(1,:);
hdfec = 3.8e-3 .* ones(1,length(sorted));
crossing(:,r) = InterX([hdfec(:)';xAxis],[berVals;xAxis]) ;
end
end
function [ber,sir,fsym] = runSIRsweep(eq_parallelization_blocklength,eq_updatelatency,eq_avg_blocklength,mudc)
% GENERATE SIR CURVE
if ismac
foldername = '/Users/silasoettinghaus/Documents/MATLAB/Labor_Datensatz_PAM4_MPI/pam4_10km_1km';
else
foldername = 'C:\Users\Silas\Documents\MATLAB\Labor_Datensatz_PAM4_MPI\pam4_10km_1km';
end
allfiles = dir(foldername);
parfor i = 1:length(allfiles)
if allfiles(i).bytes ~= 0
current_filename = allfiles(i).name;
recorded_data = load([foldername,filesep, current_filename]);
else
continue
end
results = runPostProcessing(recorded_data,mudc,eq_parallelization_blocklength,eq_updatelatency,eq_avg_blocklength);
ber(i) = results.ber;
fsym(i) = results.fsym;
sir(i) = results.sir
disp(['fsym: ',num2str(results.fsym*1e-9),'GBd, SIR: ',num2str(results.sir),' ->> BER: ',sprintf('%2E',results.ber)]);
end
end
function [best_blocklength,ber] = optimizeAvgBlocklength(recorded_data,eq_parallelization_blocklength,eq_updatelatency,eq_avg_blocklength_loop,mudc)
for j = 1:length(eq_avg_blocklength_loop)
eq_avg_blocklength = eq_avg_blocklength_loop(j);
results = runPostProcessing(recorded_data,mudc,eq_parallelization_blocklength,eq_updatelatency,eq_avg_blocklength);
ber(j) = results.ber;
disp(['fsym: ',num2str(results.fsym*1e-9),'GBd, SIR: ',num2str(results.sir),' ->> BER: ',sprintf('%2E',results.ber)]);
end
[~,pos]=min(ber);
best_blocklength = eq_avg_blocklength_loop(pos);
end
function [best_mudc,ber] = optimizeMuDc(recorded_data,eq_parallelization_blocklength,eq_updatelatency,eq_avg_blocklength,mudc_loop)
parfor j = 1:length(mudc_loop)
mudc = mudc_loop(j);
results = runPostProcessing(recorded_data,mudc,eq_parallelization_blocklength,eq_updatelatency,eq_avg_blocklength);
ber(j) = results.ber;
disp(['fsym: ',num2str(results.fsym*1e-9),'GBd, SIR: ',num2str(results.sir),' ->> BER: ',sprintf('%2E',results.ber)]);
end
[~,pos]=min(ber);
best_mudc = mudc_loop(pos);
end
function results = runPostProcessing(recorded_data,mudc,eq_parallelization_blocklength,eq_updatelatency,eq_avg_blocklength)
results.sir = recorded_data.saveStructTemp.awg2scope_keysight_state.eigenlight_mpi - 3.3 + 7.2;
results.fsym = recorded_data.saveStructTemp.common.f_sym;
results.fdac = recorded_data.saveStructTemp.common.f_DAC;
results.fadc = recorded_data.saveStructTemp.common.f_ADC;
% 0) build RX Signal
y_rx = Electricalsignal(recorded_data.saveStructTemp.dp_tsynch_out');
y_rx.fs = 2.*results.fsym;
% 0) build tx reference Signal for eq training
y_digimod = Electricalsignal(recorded_data.saveStructTemp.digi_mod_out');
y_digimod.fs = results.fsym;
% 1) normlaize
y_rx = y_rx.normalize("mode","rms");
eq = EQ_silas("Ne",[25,0,0],"Nb",[2,0,0],"trainlength",4096,...
"sps",2,...
"mu_dc_dd",mudc,...
"mu_dc_train",mudc,...
"mu_ffe_train",0,...
"mu_dfe_train",0.005,...
"mu_ffe_dd",[0.0004 0.0004 0.0004],...
"mu_dfe_dd",0.005,...
"ddloops",3,...
"trainloops",4,...
"eq_parallelization_blocklength",eq_parallelization_blocklength, ...
"eq_updatelatency",eq_updatelatency,...
"eq_avg_blocklength",eq_avg_blocklength);
[Eq_out] = eq.process(y_rx,y_digimod);
results.EQ_out = Eq_out;
% 3) digital demodulation object
digimod = PAMmapper(2^recorded_data.saveStructTemp.common.M,0);
%estimated/ equalized symbol sequence
d_estimated = digimod.demap(Eq_out);
%correct data symbols
d_correct = recorded_data.saveStructTemp.prms_out;
% 4) BER calculation
[totalbits,errors,results.ber,loc] = calc_ber(d_estimated.signal(1:end,:) ,d_correct(1:end,:)',"skip",0,"returnErrorLocation",1);
end
function std_per_lvl = calcleveldependentstd(rx_symbols,correct_symbols)
error_of_rx_signal = rx_symbols - correct_symbols;
levels = unique(correct_symbols);
for l = 1:4
level_amplitude = levels(l);
std_per_lvl(l) = var(( 1/64 .* movsum(error_of_rx_signal(correct_symbols==level_amplitude),[64/2,64/2]) ));
%std_per_lvl(l) = std(error_of_rx_signal(correct_symbols==level_amplitude));
end
end
function scatterleveldependent(rx_symbols,correct_symbols,f_sym)
col = cbrewer2('Paired',8);
ccnt = -1;
figure1 = figure();
levels = unique(correct_symbols);
start = 1;
ende = length(correct_symbols);
start = 30000;
ende = 40000;
for l = 1:4
ccnt = ccnt+2;
level_amplitude = levels(l);
symbols_for_lvl = NaN(1,length(correct_symbols));
symbols_for_lvl(correct_symbols==level_amplitude) = rx_symbols(correct_symbols==level_amplitude);
std_lvl(l) = std(symbols_for_lvl,'omitnan');
xax_in_sec = ((1:length(correct_symbols)) / f_sym) * 1e6;
xax_in_sec = 1:length(correct_symbols);
scatter(xax_in_sec(start:ende),symbols_for_lvl(start:ende),10,'.','MarkerFaceAlpha',0.5,'MarkerEdgeAlpha',0.5,'MarkerEdgeColor',col(ccnt,:));
hold on;
end
std_lvl = round(std_lvl,2);
disp(std_lvl);
ccnt = 0;
% Add the windowed/ smoothed curves
for l = 1:4
ccnt = ccnt+2;
level_amplitude = levels(l);
symbols_for_lvl = NaN(1,length(correct_symbols));
movmean = 1/250 .* movsum(rx_symbols(correct_symbols==level_amplitude),[250/2,250/2]);
symbols_for_lvl(correct_symbols==level_amplitude) = movmean;
nanx = isnan(symbols_for_lvl);
t = 1:numel(symbols_for_lvl);
symbols_for_lvl(nanx) = interp1(t(~nanx), symbols_for_lvl(~nanx), t(nanx));
xax_in_sec = ((1:length(correct_symbols)) / f_sym) * 1e6;
xax_in_sec = 1:length(correct_symbols);
plot(xax_in_sec(start:ende),symbols_for_lvl(start:ende),'Color',col(ccnt,:));
hold on
end
%yline(max(rx_symbols(correct_symbols==levels(2))))
if 0
annotation(figure1,'textbox',...
[0.660523809523809 0.844444444444448 0.133523809523809 0.0603174603174607],...
'String',['\sigma = ',num2str(std_lvl(4))],...
'LineWidth',1.8,...
'LineStyle','none',...
'FontSize',12,...
'FitBoxToText','off');
% Create textbox
annotation(figure1,'textbox',...
[0.667666666666665 0.642857142857147 0.133523809523809 0.0603174603174607],...
'String',['\sigma = ',num2str(std_lvl(3))],...
'LineWidth',1.8,...
'LineStyle','none',...
'FontSize',12,...
'FitBoxToText','off');
% Create textbox
annotation(figure1,'textbox',...
[0.671238095238093 0.442857142857148 0.133523809523809 0.0603174603174608],...
'String',['\sigma = ',num2str(std_lvl(2))],...
'LineWidth',1.8,...
'LineStyle','none',...
'FontSize',12,...
'FitBoxToText','off');
% Create textbox
annotation(figure1,'textbox',...
[0.670047619047616 0.265079365079371 0.133523809523809 0.0603174603174608],...
'String',['\sigma = ',num2str(std_lvl(1))],...
'LineWidth',1.8,...
'LineStyle','none',...
'FontSize',12,...
'FitBoxToText','off');
end
xlim([0, 2.6])
ylim([-2 2])
xlabel('Time in $\mu$s');
ylabel('Normalized Amplitude');
end

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projects/MPI_offline_analysis/mpi_analysis_pam4_old_notworking.m Normal file
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% parfsym = zeros(length(mudc_loop),length(allfiles));
% parsir = zeros(length(mudc_loop),length(allfiles));
% ber_wo_reduction = zeros(length(mudc_loop),length(allfiles));
% ber_w_dcremoval = zeros(length(mudc_loop),length(allfiles));
% mudc = zeros(length(mudc_loop),length(allfiles));
% blocklen = zeros(length(mudc_loop),length(allfiles));
% parameters.mudc = [0, 0.0001,0.0005, 0.001,0.005, 0.01:0.01:0.1, 0.2:0.1:1];
% parameters.eq_parallelization_blocklength = [1];
% parameters.eq_updatelatency = [1];
% parameters.eq_avg_blocklength =[0];
block_loop = [1];
for j = 1:length(mudc_loop)
mudc_current = mudc_loop(j);
block_current = block_loop(1);
parfor i = 1:length(allfiles)
if 1
if allfiles(i).bytes ~= 0
current_filename = allfiles(i).name;
else
continue
end
else
%
% current_filename = 'pam4__loop_29_56Gbd_19092023_1546.mat';
% current_filename = 'pam4__loop_30_92Gbd_19092023_1547.mat';
%
current_filename = 'pam4__loop_13_56Gbd_19092023_1512.mat';
current_filename = 'pam4__loop_14_92Gbd_19092023_1513.mat';
end
% load dataset
recorded_data = load([foldername,filesep, current_filename]);
% get common variables of simulation loop
%even if redundant, save stuff to results struct
parsir(j,i) = recorded_data.saveStructTemp.awg2scope_keysight_state.eigenlight_mpi - 3.3 + 7.2;
parfsym(j,i) = recorded_data.saveStructTemp.common.f_sym;
fdac = recorded_data.saveStructTemp.common.f_DAC;
fadc = recorded_data.saveStructTemp.common.f_ADC;
fsym = recorded_data.saveStructTemp.common.f_sym;
% 0) build RX Signal
y_rx = Electricalsignal(recorded_data.saveStructTemp.dp_tsynch_out');
y_rx.fs = 2.*fsym;
% 0) build tx reference Signal for eq training
y_digimod = Electricalsignal(recorded_data.saveStructTemp.digi_mod_out');
y_digimod.fs = fsym;
rx_symbols = y_rx.resample("fs_in",2.*fsym,"fs_out",fsym);
rx_symbols = rx_symbols.normalize("mode","rms").signal;
correct_symbols = recorded_data.saveStructTemp.digi_mod_out';
% scatterleveldependent(rx_symbols,correct_symbols,parfsym(j,i));
% title([num2str(fsym.*1e-9),' GBd; SIR: ',num2str(parsir(j,i)),' dB']);
% if parfsym(j,i) == 56e9
% deviation56(i) = var(( 1/500 .* movsum(y_rx.signal,[500/2,500/2]) ));
% sir56(i) = simdata.saveStructTemp.awg2scope_keysight_state.eigenlight_mpi - 3.3 + 7.2;
% std56(i,:) = calcleveldependentstd(rx_symbols,correct_symbols);
% else
% deviation92(i) = var(( 1/500 .* movsum(y_rx.signal,[500/2,500/2]) ));
% sir92(i) = simdata.saveStructTemp.awg2scope_keysight_state.eigenlight_mpi - 3.3 + 7.2;
% std92(i,:) = calcleveldependentstd(rx_symbols,correct_symbols);
% end
% 1) normlaize
y_rx = y_rx.normalize("mode","rms");
eq = EQ_silas("Ne",[25,0,0],"Nb",[2,0,0],"trainlength",4096,...
"sps",2,...
"mu_dc_dd",mudc_current,...
"mu_dc_train",mudc_current,...
"mu_ffe_train",0,...
"mu_dfe_train",0.005,...
"mu_ffe_dd",[0.0004 0.0004 0.0004],...
"mu_dfe_dd",0.005,...
"ddloops",3,...
"trainloops",4,...
"eq_parallelization_blocklength",block_current, ...
"eq_updatelatency",3,...
"eq_avg_blocklength",10000);
[Eq_out] = eq.process(y_rx,y_digimod);
% 2.2) MPI reduction blocks
% 3) digital demodulation
digimod = PAMmapper(2^recorded_data.saveStructTemp.common.M,0);
d_hat = digimod.demap(Eq_out);
d_ = digimod.demap(Electricalsignal(recorded_data.saveStructTemp.digi_mod_out'));
d = recorded_data.saveStructTemp.prms_out;
% scatterleveldependent(Eq_out.signal,correct_symbols,parfsym(j,i));
% title([num2str(fsym.*1e-9),' GBd; SIR: ',num2str(parsir(j,i)),' dB']);
% 4) BER calculation
[totalbits,errors_bm,ber_dhat,loc] = calc_ber(d_hat.signal(1:end,:) ,d(1:end,:)',"skip",0,"returnErrorLocation",1);
ber_nodctap = mean(recorded_data.saveStructTemp.prms_compare_state.BER );
disp(['fsym: ',num2str(parfsym(j,i).*1e-9),'GBd, SIR: ',num2str(parsir(j,i)),' ->> BER_new: ',sprintf('%2E',ber_dhat),' BER_old: ',sprintf('%2E',ber_nodctap)]);
% 5) save to struct
ber_wo_reduction(j,i) = ber_nodctap;
ber_w_dcremoval(j,i) = ber_dhat;
mudc(j,i) = eq.mu_dc_dd;
blocklen(j,i) = eq.eq_parallelization_blocklength;
end
end
rate = [92e9, 56e9];
figure(95)
col = cbrewer2('Paired',8);
for r = 1:length(rate)
for j = 1:length(mudc_loop)
if mudc_loop(j) == 0
lnsty = '--';
mark = 'square';
else
lnsty = "-";
mark = 'v';
end
if r == 1
handlvis = 'on';
colr = col(6,:);
else
handlvis = 'off';
colr = col(8,:);
end
%plot curve of standard FFE+DFE
if j == 1
load(['C:\Users\Silas\Nextcloud4\Dokumente\02_Ablage_Office\OFC2024\',num2str(rate(r).*1e-9),'GBd_no_dcs.mat'],'b');
plot(abs(b(1,:)),b(2,:),'DisplayName',[num2str(rate(r).*1e-9),' GBd; PAM4; mudc: ',0],'LineWidth',1,'Marker','o','MarkerSize',5,'LineStyle',lnsty,'HandleVisibility',handlvis,'Color',colr);
end
hold on
b = [parsir(j,parfsym(j,:)==rate(r));ber_w_dcremoval(j,parfsym(j,:)==rate(r))];
b=sort(b,2);
plot(abs(b(1,:)),b(2,:),'DisplayName',[num2str(rate(r).*1e-9),' GBd; PAM4; mudc: ',num2str(mudc_loop(j))],'LineWidth',1,'Marker',mark,'MarkerSize',5,'LineStyle',lnsty,'HandleVisibility',handlvis,'Color',colr);
end
end
set(gca, 'YScale', 'log');
yline(3.8e-3,'LineWidth',1, 'LineStyle','--','HandleVisibility','off');
xlim([15,35]);
xlabel('SIR in dB');
ylabel('BER');
annotation('textbox',...
[0.328380952380952 0.477777777777781 0.197809523809524 0.106349206349207],...
'String',{'3.8 $\cdot 10^{-3}$'},...
'LineStyle','none',...
'Interpreter','latex',...
'FitBoxToText','off');
% legend('Standard EQ','Adaptive DC Subtraction')
figure(16)
hold on
plot(mudc_loop,ber_w_dcremoval(:,1));set(gca, 'YScale', 'log');set(gca, 'XScale', 'log');
xlim([0,1])