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Minor Changes here and there

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This commit is contained in:
Silas Oettinghaus
2024-08-16 10:08:50 +02:00
parent 3f089dca9f
commit 793a5b7efd
7 changed files with 100 additions and 68 deletions
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14
Classes/03_receive/Photodiode.m
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@@ -7,7 +7,7 @@ classdef Photodiode
responsivity responsivity
dark_current dark_current
temperature temperature
nep
randomkey randomkey
randomstream randomstream
@@ -22,6 +22,7 @@ classdef Photodiode
options.responsivity = 1; options.responsivity = 1;
options.dark_current = 0; options.dark_current = 0;
options.temperature = 20; options.temperature = 20;
options.nep = 0; %(Moveit IMDD Standard: 1.8e-11) noise effective power in pA/sqrt(Hz); usually between 10-20 pA; see J.Leibrich Diss/ S. Pachnicke Slides
options.randomkey = 1; options.randomkey = 1;
end end
@@ -71,11 +72,16 @@ classdef Photodiode
yout = yout + shot_noise; yout = yout + shot_noise;
% Thermal Noise % Thermal Noise
therm_current_psd = (2 * k * T / R ) ; %squared % NEP is noise equivalent power, see Dissertation j. Leibrich
therm_current_psd = 1.8e-11^2; % P. 121 or Stephan Pachnicke Optical Comm. Lecture Slides
if obj.nep == 0
nep_squared = (2 * k * T / R ) ; %squared
else
nep_squared = obj.nep^2;
end
Bw = obj.fsimu; Bw = obj.fsimu;
therm_noise_pow = therm_current_psd * Bw; %squared therm_noise_pow = nep_squared * Bw; %squared
therm_noise = sqrt(therm_noise_pow) .* randn(obj.randomstream,size(yout,1),1); therm_noise = sqrt(therm_noise_pow) .* randn(obj.randomstream,size(yout,1),1);

2
Classes/04_DSP/FFE.m
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@@ -140,6 +140,8 @@ classdef FFE < handle
err(symbol) = y(symbol) - d_hat(symbol); % Instantaneous error err(symbol) = y(symbol) - d_hat(symbol); % Instantaneous error
true_err(symbol) = y(symbol) - d(symbol); % Instantaneous error
if mio ~= 0 if mio ~= 0
obj.e = obj.e - (mio * err(symbol) * U) ; % Weight update rule of LMS obj.e = obj.e - (mio * err(symbol) * U) ; % Weight update rule of LMS
else else

2
Classes/04_DSP/FFE_DCremoval.m
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@@ -101,7 +101,7 @@ classdef FFE_DCremoval < handle
err = 0; err = 0;
% e_dc_buffer = NaN(100,1); % e_dc_buffer = NaN(100,1);
e_dc_buffer = NaN(10,1); e_dc_buffer = NaN(1,1);
e_dc_est = 0; e_dc_est = 0;
for epoch = 1 : epochs for epoch = 1 : epochs

26
Classes/04_DSP/FFE_FFDCAVG.m
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@@ -126,10 +126,13 @@ classdef FFE_FFDCAVG < handle
end end
for epoch = 1 : epochs for epoch = 1 : epochs
symbol = 0; symbol = 0;
err_buffer = zeros(numel(obj.constellation),50);
err_buffer = zeros(numel(obj.constellation),90);
dc_err = zeros(numel(obj.constellation),1);
dc_sto = NaN(numel(obj.constellation),N);
for sample = 1 : obj.sps : N for sample = 1 : obj.sps : N
symbol = symbol+1; symbol = symbol+1;
@@ -141,17 +144,30 @@ classdef FFE_FFDCAVG < handle
if training if training
[~,symbol_idx] = min(abs(d(symbol) - obj.constellation)); % decision for closest constellation point [~,symbol_idx] = min(abs(d(symbol) - obj.constellation)); % decision for closest constellation point
d_hat(symbol,1) = d(symbol); d_hat(symbol,1) = d(symbol);
else
always_correct_decision = 0;
if always_correct_decision
[~,symbol_idx] = min(abs(d(symbol) - obj.constellation)); % decision for closest constellation point
else else
[~,symbol_idx] = min(abs(y(symbol) - obj.constellation)); % decision for closest constellation point [~,symbol_idx] = min(abs(y(symbol) - obj.constellation)); % decision for closest constellation point
end
d_hat(symbol,1) = obj.constellation(symbol_idx); d_hat(symbol,1) = obj.constellation(symbol_idx);
end end
err(symbol) = y(symbol) - d_hat(symbol); % Instantaneous error err(symbol) = y(symbol) - d_hat(symbol); % Instantaneous error
if 1
%use buffer for dc-error
err_buffer(symbol_idx,1) = err(symbol); err_buffer(symbol_idx,1) = err(symbol);
err_buffer(symbol_idx,:) = circshift(err_buffer(symbol_idx,:),1); err_buffer(symbol_idx,:) = circshift(err_buffer(symbol_idx,:),1);
dc_sto(symbol_idx,symbol) = obj.mu_buff*mean(err_buffer(symbol_idx,:));
y(symbol) = y(symbol) - obj.mu_buff*mean(err_buffer(symbol_idx,:)); y(symbol) = y(symbol) - obj.mu_buff * mean(err_buffer(symbol_idx,:));
else
%or use 1+alpha*D as adaptive error
dc_err(symbol_idx) = dc_err(symbol_idx) + obj.mu_buff * err(symbol);
dc_sto(symbol_idx,symbol) = dc_err(symbol_idx);
y(symbol) = y(symbol) - dc_err(symbol_idx);
end
if training if training
[~,symbol_idx] = min(abs(d(symbol) - obj.constellation)); % decision for closest constellation point [~,symbol_idx] = min(abs(d(symbol) - obj.constellation)); % decision for closest constellation point
@@ -173,8 +189,10 @@ classdef FFE_FFDCAVG < handle
if mod(sample,100) == 1 && showviz if mod(sample,100) == 1 && showviz
a2.XData = 1:2*numel(y); a2.XData = 1:2*numel(y);
a2.YData = repelem(y, 2); a2.YData = repelem(y, 2);
a3.XData = 1:2*numel(d_hat); a3.XData = 1:2*numel(d_hat);
a3.YData = repelem(d_hat, 2); a3.YData = repelem(d_hat, 2);
a4.Value = sample; a4.Value = sample;
% b.YData = x(symbol:symbol+500); % b.YData = x(symbol:symbol+500);
c.YData = obj.e; c.YData = obj.e;

2
projects/MPI_August/EQ_development_ffe_dc_avg.m
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@@ -9,7 +9,7 @@ Bits = load("projects/MPI_August/bits.mat","Bits");
Bits = Bits.Bits; Bits = Bits.Bits;
mubuff = [0.7]; mubuff = [0.5];
for m = 1:numel(mubuff) for m = 1:numel(mubuff)

35
projects/MPI_August/auswertung/sir_curve.m Normal file
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@@ -0,0 +1,35 @@
M = wh.parameter.M.values(1);
datarate = wh.parameter.datarate.values(1);
sir = wh.parameter.sir.values;
laser_linewidth = wh.parameter.laser_linewidth.values(1);
pn_key = wh.parameter.pn_key.values;
rop = wh.parameter.rop.values(1);
cols = linspecer(4);
cnt = 0;
for pnk = pn_key
cnt = cnt+1;
ber_ffe = wh.getStoValue('ber_ffe',M,datarate,sir,laser_linewidth,pnk,rop);
ber_dcavg = wh.getStoValue('ber_dcavg',M,datarate,sir,laser_linewidth,pnk,rop);
ber_adapt = wh.getStoValue('ber_adapt',M,datarate,sir,laser_linewidth,pnk,rop);
ber_derem = wh.getStoValue('ber_dcrem',M,datarate,sir,laser_linewidth,pnk,rop);
% Create the initial plot
figure(43);
hold on; % Retain the plot so new points can be added without complete redraw
plot(sir,ber_ffe',"LineWidth",1,"LineStyle","-","Marker",".","MarkerSize",10,"DisplayName","ber ffe",'Color',cols(1,:));
plot(sir,ber_dcavg',"LineWidth",1,"LineStyle","-","Marker",".","MarkerSize",10,"DisplayName","ber dcavg",'Color',cols(2,:));
plot(sir,ber_adapt',"LineWidth",1,"LineStyle","-","Marker",".","MarkerSize",10,"DisplayName","ber adapt",'Color',cols(3,:));
plot(sir,ber_derem',"LineWidth",1,"LineStyle","-","Marker",".","MarkerSize",10,"DisplayName","ber dcrem",'Color',cols(4,:));
end
yline(3.8e-3,'DisplayName','HD-FEC');
xlabel('Signal to Interference Ratio (dB)');
ylabel('Bit Error Rate (BER)');
title('Bit Error Rate vs. SIR');
set(gca,'yscale','log');
grid on;
legend

77
projects/MPI_August/mpi_uncorrelated.m
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@@ -2,17 +2,18 @@
%% Parameter to simulate and save %% Parameter to simulate and save
params = struct; params = struct;
params.M = [8]; params.M = [4];
params.datarate = [224]; params.datarate = [224];
params.sir = [30]; %decibel = attenuation of interference path params.sir = [15:45]; %decibel = attenuation of interference path
params.laser_linewidth = [1e6]; params.laser_linewidth = [1e6];
params.pn_key = [1]; params.pn_key = [1];
params.rop = [-12:0]; params.rop = [-7];
name = ['wh_',strrep(num2str(now),'.','')]; name = ['wh_',strrep(num2str(now),'.','')];
wh = DataStorage(params); wh = DataStorage(params);
wh.addStorage("ber_ffe"); wh.addStorage("ber_ffe");
wh.addStorage("ber_dcavg"); wh.addStorage("ber_dcavg");
wh.addStorage("ber_adapt"); wh.addStorage("ber_adapt");
@@ -37,7 +38,7 @@ for M = wh.parameter.M.values
M8199 = M8199A("kover",kover); M8199 = M8199A("kover",kover);
fdac = M8199.fdac; fdac = M8199.fdac;
fsym = round(datarate / log2(M))*1e9; fsym = round(datarate / log2(M))*1e9;
rrcalpha = 0.05;
Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rrc","pulselength",16,"rrcalpha",rrcalpha); Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rrc","pulselength",16,"rrcalpha",rrcalpha);
% MAIN SIGNAL % MAIN SIGNAL
@@ -88,38 +89,43 @@ for M = wh.parameter.M.values
j_ = wh.parameter.sir.length; j_ = wh.parameter.sir.length;
i_ = wh.parameter.rop.length; i_ = wh.parameter.rop.length;
ber=zeros(j_,i_);
ber_ffe=zeros(j_,i_);
ber_dcavg=zeros(j_,i_);
ber_adapt=zeros(j_,i_);
ber_dcrem=zeros(j_,i_);
patten=zeros(j_,i_); patten=zeros(j_,i_);
for j = 1:j_ parfor j = 1:j_
sir = wh.parameter.sir.values(j); sir = wh.parameter.sir.values(j);
%%%%% Interference Signal Fiber Prop %%%%%% %%%%% Interference Signal Fiber Prop %%%%%%
Opt_sig_i = Fiber("fsimu",Opt_sig_i.fs,"fiber_length",link_length/1000,"alpha",0.3,"D",0,"lambda0",1310,"gamma",0,"Dslope",0.07).process(Opt_sig_i); Opt_sig_i_prop = Fiber("fsimu",Opt_sig_i.fs,"fiber_length",link_length/1000,"alpha",0.3,"D",0,"lambda0",1310,"gamma",0,"Dslope",0.07).process(Opt_sig_i);
Opt_sig_i = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power","amplification_db",Opt_sig.power-sir).process(Opt_sig_i); Opt_sig_i_prop = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power","amplification_db",Opt_sig.power-sir).process(Opt_sig_i_prop);
%%%%% ADD Interference and Main Signal %%%%%% %%%%% ADD Interference and Main Signal %%%%%%
Opt_sig = Opt_sig_i + Opt_sig; Opt_sig_comb = Opt_sig_i_prop + Opt_sig;
%%%%% Interference Signal Fiber Prop %%%%%% %%%%% Interference Signal Fiber Prop %%%%%%
Opt_sig = Fiber("fsimu",Opt_sig.fs,"fiber_length",link_length/1000,"alpha",0.3,"D",0,"lambda0",1310,"gamma",0,"Dslope",0.07).process(Opt_sig); Opt_sig_comb = Fiber("fsimu",Opt_sig_comb.fs,"fiber_length",link_length/1000,"alpha",0.3,"D",0,"lambda0",1310,"gamma",0,"Dslope",0.07).process(Opt_sig_comb);
% % MPI Channel % % MPI Channel
% Opt = channel_model_mpi(Opt_sig,link_length,mpi_path,sir); % Opt = channel_model_mpi(Opt_sig,link_length,mpi_path,sir);
% Receiver ROP curve % Receiver ROP curve
parfor i = 1:i_ for i = 1:i_
rop=wh.parameter.rop.values(i); rop=wh.parameter.rop.values(i);
% Set ROP % Set ROP
Rx_sig = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power","amplification_db",rop).process(Opt_sig); Rx_sig = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power","amplification_db",rop).process(Opt_sig_comb);
patten(j,i) = Rx_sig.power; patten(j,i) = Rx_sig.power;
%%%%%% Square Law %%%%%% %%%%%% Square Law %%%%%%
Rx_sig = Photodiode("fsimu",fdac*kover,"dark_current",2e-08,"responsivity",1,"temperature",20).process(Rx_sig); Rx_sig = Photodiode("fsimu",fdac*kover,"dark_current",2e-08,"responsivity",1,"temperature",20,"nep",1.8e-11).process(Rx_sig);
%%%%%% Lowpass PhDiode %%%%%% %%%%%% Lowpass PhDiode %%%%%%
Rx_sig = Filter('filtdegree',2,"f_cutoff",70e9,"fs",fdac*kover,"filterType",filtertypes.gaussian,"active",true).process(Rx_sig); Rx_sig = Filter('filtdegree',2,"f_cutoff",70e9,"fs",fdac*kover,"filterType",filtertypes.gaussian,"active",true).process(Rx_sig);
@@ -153,7 +159,8 @@ for M = wh.parameter.M.values
[~,errors_bm,ber_dcavg(j,i),errors] = calc_ber(Rx_bits.signal,Bits.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1); [~,errors_bm,ber_dcavg(j,i),errors] = calc_ber(Rx_bits.signal,Bits.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
disp(['BER: ',sprintf('%.1E',ber_dcavg(j,i)),' - - ROP: ',num2str(patten(j,i)),'dBm - - PAM-',num2str(M),' - - ',num2str(fsym*1e-9),' GBd']); disp(['BER: ',sprintf('%.1E',ber_dcavg(j,i)),' - - ROP: ',num2str(patten(j,i)),'dBm - - PAM-',num2str(M),' - - ',num2str(fsym*1e-9),' GBd']);
Eq = FFE_adaptive_decision("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",25,"sps",2,"decide",1,"buffer_length",85); [EQ_sig] = Eq.process(Scpe_sig,Symbols); Eq = FFE_adaptive_decision("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",25,"sps",2,"decide",1,"buffer_length",85);
[EQ_sig] = Eq.process(Scpe_sig,Symbols);
Rx_bits = PAMmapper(M,0).demap(EQ_sig); Rx_bits = PAMmapper(M,0).demap(EQ_sig);
[~,errors_bm,ber_adapt(j,i),errors] = calc_ber(Rx_bits.signal,Bits.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1); [~,errors_bm,ber_adapt(j,i),errors] = calc_ber(Rx_bits.signal,Bits.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
disp(['BER: ',sprintf('%.1E',ber_adapt(j,i)),' - - ROP: ',num2str(patten(j,i)),'dBm - - PAM-',num2str(M),' - - ',num2str(fsym*1e-9),' GBd']); disp(['BER: ',sprintf('%.1E',ber_adapt(j,i)),' - - ROP: ',num2str(patten(j,i)),'dBm - - PAM-',num2str(M),' - - ',num2str(fsym*1e-9),' GBd']);
@@ -182,10 +189,10 @@ for M = wh.parameter.M.values
end end
toc toc
disp(['Simulated: ',num2str(cnt/endcnt*100),' %']); disp(['Simulated: ',num2str(cnt/endcnt*100),' %']);
save(['C:\Users\Silas\Documents\MATLAB\imdd_simulation\projects\MPI_Juni\',name,'.mat'],"wh"); wh.save('C:\Users\Silas\Documents\MATLAB\imdd_simulation\projects\MPI_August\auswertung\')
end end
end end
end end
@@ -193,42 +200,6 @@ end
M = wh.parameter.M.values(1);
datarate = wh.parameter.datarate.values(1);
sir = wh.parameter.sir.values(1);
laser_linewidth = wh.parameter.laser_linewidth.values(1);
pn_key = wh.parameter.pn_key.values;
rop = wh.parameter.rop.values;
cols = linspecer(4);
cnt = 0;
for pnk = pn_key
cnt = cnt+1;
ber_ffe = wh.getStoValue('ber_ffe',M,datarate,sir,laser_linewidth,pnk,rop);
ber_dcavg = wh.getStoValue('ber_dcavg',M,datarate,sir,laser_linewidth,pnk,rop);
ber_adapt = wh.getStoValue('ber_adapt',M,datarate,sir,laser_linewidth,pnk,rop);
ber_derem = wh.getStoValue('ber_dcrem',M,datarate,sir,laser_linewidth,pnk,rop);
% Create the initial plot
figure(43);
hold on; % Retain the plot so new points can be added without complete redraw
plot(rop,ber_ffe',"LineWidth",1,"LineStyle",":","Marker",".","MarkerSize",10,"DisplayName","ber ffe",'Color',cols(1,:));
plot(rop,ber_dcavg',"LineWidth",1,"LineStyle",":","Marker",".","MarkerSize",10,"DisplayName","ber dcavg",'Color',cols(2,:));
plot(rop,ber_adapt',"LineWidth",1,"LineStyle",":","Marker",".","MarkerSize",10,"DisplayName","ber adapt",'Color',cols(3,:));
plot(rop,ber_derem',"LineWidth",1,"LineStyle",":","Marker",".","MarkerSize",10,"DisplayName","ber derem",'Color',cols(4,:));
end
yline(3.8e-3,'DisplayName','HD-FEC');
xlabel('Signal to Interference Ratio (dB)');
ylabel('Bit Error Rate (BER)');
title('Bit Error Rate vs. SIR (MPI)');
set(gca,'yscale','log');
grid on;
legend