small updates from work pc
try to implement kalman filter for MPI mitigation
This commit is contained in:
sioe
@@ -284,7 +284,7 @@ classdef Signal
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N = 2^(nextpow2(length(obj.signal))-8);
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[p_lin,w] = pwelch(obj.signal,hanning(N),N/2,N,obj.fs,"centered","power","mean");
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normalize = 1;
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normalize = 0;
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if normalize
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p_lin = p_lin./ max(p_lin);
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p_dbm = 10*log10(p_lin); %dB to dBm in case of "power"
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@@ -104,10 +104,10 @@ classdef EQ
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end
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function [signalclass_out,error_log] = process(obj,signalclass_in, reference_signalclass_in)
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function [signalclass_out,noi] = process(obj,signalclass_in, reference_signalclass_in)
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% actual processing of the signal (steps 1. - 3.)
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[signalclass_in.signal,error_log] = obj.process_(signalclass_in.signal', reference_signalclass_in.signal');
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[signalclass_in.signal] = obj.process_(signalclass_in.signal', reference_signalclass_in.signal');
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signalclass_in.signal = signalclass_in.signal';
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@@ -120,6 +120,9 @@ classdef EQ
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% write to output
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signalclass_out = signalclass_in;
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noi = signalclass_out;
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noi = signalclass_out - reference_signalclass_in;
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end
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function [yout,error_log] = process_(obj,data_in,ref_in)
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@@ -102,29 +102,6 @@ classdef FFE < handle
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x = [zeros(floor(obj.order/2),1); x; zeros(obj.order,1)];
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if showviz
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f = figure(111);
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subplot(2,2,1:2);
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hold on
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a = scatter(1:numel(x),x,1,'.');
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a2 = scatter(1,1,1,'.');
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a3 = scatter(1,1,2,'.');
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a4 = xline(1);
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ylim([-3 3])
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xlim([0 length(x)]);
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% subplot(2,2,3)
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% dplot = x(1:1+500);
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% b = scatter(1:numel(dplot),dplot,5,'x');
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% xline(1)
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% xline(obj.order)
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% ylim([-3 3])
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% xlim([0 500]);
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subplot(2,2,3:4)
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c = stem(obj.e);
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ylim([-1 1])
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drawnow
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end
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for epoch = 1 : epochs
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symbol = 0;
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for sample = 1 : obj.sps : N
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@@ -153,16 +130,6 @@ classdef FFE < handle
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obj.e = obj.e - err(symbol) * U / normalizationfactor; % Weight update rule of NLMS
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end
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if mod(sample,100) == 1 && showviz
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a2.XData = 1:2*numel(y);
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a2.YData = repelem(y, 2);
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a3.XData = 1:2*numel(d_hat);
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a3.YData = repelem(d_hat, 2);
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a4.Value = sample;
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% b.YData = x(symbol:symbol+500);
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c.YData = obj.e;
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drawnow;
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end
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obj.error(epoch,symbol) = err(symbol) * err(symbol)'; % Instantaneous square error
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@@ -59,7 +59,7 @@ classdef FFE_DCremoval < handle
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end
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function [X] = process(obj, X, D)
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function [X,Noi] = process(obj, X, D)
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% actual processing of the signal (steps 1. - 3.)
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% 1 normalize RMS
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@@ -86,6 +86,8 @@ classdef FFE_DCremoval < handle
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lbdesc = [num2str(obj.order),' tap FFE'];
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X = X.logbookentry(lbdesc); % append to logbook
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Noi = X;
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Noi = X - D;
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end
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@@ -146,11 +148,21 @@ classdef FFE_DCremoval < handle
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e_dc_buffer = circshift(e_dc_buffer,1);
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end
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e_dc_save(symbol) = e_dc_est;
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obj.error(epoch,symbol) = err(symbol) * err(symbol)'; % Instantaneous square error
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end
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end
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if ~training
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figure(1122)
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hold on
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scatter(1:numel(e_dc_save),e_dc_save,1,'.');
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scatter(1:numel(y),y,1,'.');
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end
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end
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end
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@@ -102,29 +102,6 @@ classdef FFE_FFDCAVG < handle
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x = [zeros(floor(obj.order/2),1); x; zeros(obj.order,1)];
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if showviz
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f = figure(111);
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subplot(2,2,1:2);
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hold on
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a = scatter(1:numel(x),x,1,'.');
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a2 = scatter(1,1,1,'.');
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a3 = scatter(1,1,2,'.');
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a4 = xline(1);
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ylim([-3 3])
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xlim([0 length(x)]);
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% subplot(2,2,3)
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% dplot = x(1:1+500);
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% b = scatter(1:numel(dplot),dplot,5,'x');
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% xline(1)
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% xline(obj.order)
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% ylim([-3 3])
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% xlim([0 500]);
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subplot(2,2,3:4)
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c = stem(obj.e);
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ylim([-1 1])
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drawnow
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end
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for epoch = 1 : epochs
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symbol = 0;
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@@ -186,18 +163,6 @@ classdef FFE_FFDCAVG < handle
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obj.e = obj.e - err(symbol) * U / normalizationfactor; % Weight update rule of NLMS
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end
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if mod(sample,100) == 1 && showviz
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a2.XData = 1:2*numel(y);
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a2.YData = repelem(y, 2);
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a3.XData = 1:2*numel(d_hat);
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a3.YData = repelem(d_hat, 2);
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a4.Value = sample;
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% b.YData = x(symbol:symbol+500);
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c.YData = obj.e;
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drawnow;
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end
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obj.error(epoch,symbol) = err(symbol) * err(symbol)'; % Instantaneous square error
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245
Classes/04_DSP/Equalizer/FFE_Kalman.m
Normal file
245
Classes/04_DSP/Equalizer/FFE_Kalman.m
Normal file
@@ -0,0 +1,245 @@
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classdef FFE_Kalman < handle
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% Implementation of plain and simple FFE.
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% 1) Training mode (stable performance when you use NLMS)
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% 2) Decision directed mode
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% Eq = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",25,"sps",2,"decide",0);
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properties
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sps % usually 2
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order
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e
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error
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len_tr
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mu_tr
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epochs_tr
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mu_dd
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epochs_dd
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constellation
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decide
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end
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methods
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function obj = FFE_Kalman(options)
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arguments(Input)
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options.sps = 2;
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options.order = 15;
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options.len_tr = 4096;
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options.mu_tr = 0;
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options.epochs_tr = 5;
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options.mu_dd = 1e-5;
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options.epochs_dd = 5;
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options.decide = false;
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end
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fn = fieldnames(options);
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for n = 1:numel(fn)
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obj.(fn{n}) = options.(fn{n});
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end
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obj.e = zeros(obj.order,1);
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obj.error = 0;
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end
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function [X,Noi] = process(obj, X, D)
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% actual processing of the signal (steps 1. - 3.)
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% 1 normalize RMS
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X = X.normalize("mode","rms");
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obj.constellation = unique(D.signal);
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% Training Mode
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training = 1;
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showviz = 0;
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obj.equalize(X.signal, D.signal,obj.mu_tr,obj.epochs_tr,obj.len_tr,training,showviz);
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% Decision Directed Mode
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n = X.length;
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training = 0;
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showviz = 0;
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[signal,decision]=obj.equalize(X.signal, D.signal,obj.mu_dd,obj.epochs_dd,n,training,showviz);
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% Output Signal
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if obj.decide
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X.signal = decision;
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else
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X.signal = signal;
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end
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X.fs = D.fs; %change sampling frequency of outgoing signal from fdac e.g. 2 sps to symbol spaced = fsym
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lbdesc = [num2str(obj.order),' tap FFE'];
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X = X.logbookentry(lbdesc); % append to logbook
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Noi = X;
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Noi = X - D;
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end
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function [y,d_hat] = equalize(obj,x,d,mio,epochs,N,training,showviz)
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arguments
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obj
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x
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d
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mio
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epochs
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N
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training
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showviz
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end
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x = [zeros(floor(obj.order/2),1); x; zeros(obj.order,1)];
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for epoch = 1 : epochs
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symbol = 0;
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% Initialization of Kalman filter variables
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A = 1; % State transition matrix
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H = 1; % Observation matrix
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Q = 1e-4; % Process noise covariance
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R = 1e-1; % Measurement noise covariance
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P = 1; % Initial error covariance
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mpi_est = 0; % Initial estimate for MPI noise
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K = 0; % Kalman gain
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subtract_mpi_est = 1;
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for sample = 1 : obj.sps : N
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symbol = symbol + 1;
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% Get the current input sample and the equalizer output
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U = x(obj.order + sample - 1 : -1 : sample);
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if subtract_mpi_est || ~training
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y(symbol,1) = (obj.e.' * U) - mpi_est .* 1 ; % Subtract MPI estimate
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else
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y(symbol,1) = obj.e.' * U;
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end
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% Decision and error calculation
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if training
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d_hat(symbol,1) = d(symbol);
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else
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[~, symbol_idx] = min(abs(y(symbol) - obj.constellation)); % Closest constellation point
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d_hat(symbol,1) = obj.constellation(symbol_idx);
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end
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err(symbol) = y(symbol) - d_hat(symbol); % Instantaneous residual error
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true_err(symbol) = y(symbol) - d(symbol);
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% Kalman filter update to track the MPI noise
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% Prediction step
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P = A * P * A' + Q; % Update error covariance
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K = P * H' / (H * P * H' + R); % Kalman gain
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% Update step
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mpi_est_new = mpi_est + K * (err(symbol) - H * mpi_est); % MPI noise estimation
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alpha=0;
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mpi_est = alpha * mpi_est + (1 - alpha) * mpi_est_new;
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P = (1 - K * H) * P; % Update error covariance
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% Subtract MPI noise from the signal
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if subtract_mpi_est || ~training
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y(symbol) = y(symbol);% - mpi_est;
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else
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end
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% Equalizer weight update (LMS or NLMS)
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if mio ~= 0
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obj.e = obj.e - (mio * err(symbol) * U); % LMS weight update
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else
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normalizationfactor = (U.' * U);
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obj.e = obj.e - err(symbol) * U / normalizationfactor; % NLMS weight update
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end
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% Store MPI estimate for visualization
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mpi_estimates(symbol) = mpi_est;
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Kgain(symbol) = K;
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P_(symbol) = P;
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H_(symbol) = H;
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end %symbols
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end %epoch
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if ~training
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if 1
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% figure;
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% subplot(2,2,1)
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% hold on
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% scatter(1:numel(y),y,1,'.');
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% plot(1:numel(mpi_estimates), mpi_estimates);
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% subplot(2,2,3)
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% plot(1:numel(true_err), true_err);
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% subplot(2,2,2)
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% scatter(1:numel(y),y'-mpi_estimates,1,'.');
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% xlabel('Sample Index');
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% ylabel('MPI Noise Estimate');
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% title('MPI Noise Estimation Over Time (Kalman Filter)');
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% grid on;
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figure(111);
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subplot(3,1,1)
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hold on
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cla
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scatter(1:numel(y),y,1,'.');
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plot(1:numel(mpi_estimates), mpi_estimates,'DisplayName','mpi est');
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ylim([-2,2]);
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subplot(3,1,2)
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cla
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plot(1:numel(true_err), true_err,'DisplayName','true err');
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ylim([-1,1]);
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subplot(3,1,3)
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plot(1:numel(true_err), true_err-mpi_estimates,'DisplayName',['diff rms: ',num2str(rms(true_err-mpi_estimates))]);
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ylim([-1,1]);
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legend
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figure(333)
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hold on
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von = 5000;
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bis = 15000;
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plot(von:bis,true_err(von:bis),'DisplayName','Error')
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plot(von:bis,movmean(true_err(von:bis), [30,30]),'DisplayName','Error')
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plot(von:bis,mpi_estimates(von:bis),'DisplayName','Est','LineWidth',2);
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legend
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figure(222)
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hold on
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crr = xcorr(mpi_estimates,true_err,'normalized');
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plot(crr);
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end
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% y = y-mpi_estimates';
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end
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end
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end
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end
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197
Classes/04_DSP/Equalizer/FFE_Kalman_Feedback.m
Normal file
197
Classes/04_DSP/Equalizer/FFE_Kalman_Feedback.m
Normal file
@@ -0,0 +1,197 @@
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classdef FFE_Kalman_Feedback < handle
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% Implementation of plain and simple FFE.
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% 1) Training mode (stable performance when you use NLMS)
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% 2) Decision directed mode
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% Eq = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",25,"sps",2,"decide",0);
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properties
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sps % usually 2
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order
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e
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error
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len_tr
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mu_tr
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epochs_tr
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mu_dd
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epochs_dd
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constellation
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decide
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end
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methods
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function obj = FFE_Kalman_Feedback(options)
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arguments(Input)
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options.sps = 2;
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options.order = 15;
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options.len_tr = 4096;
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options.mu_tr = 0;
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options.epochs_tr = 5;
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options.mu_dd = 1e-5;
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options.epochs_dd = 5;
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options.decide = false;
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end
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fn = fieldnames(options);
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for n = 1:numel(fn)
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obj.(fn{n}) = options.(fn{n});
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end
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obj.e = zeros(obj.order,1);
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obj.error = 0;
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end
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function [X,Noi] = process(obj, X, D)
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% actual processing of the signal (steps 1. - 3.)
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% 1 normalize RMS
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X = X.normalize("mode","rms");
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obj.constellation = unique(D.signal);
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% Training Mode
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training = 1;
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showviz = 0;
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obj.equalize(X.signal, D.signal,obj.mu_tr,obj.epochs_tr,obj.len_tr,training);
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% Decision Directed Mode
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n = X.length;
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training = 0;
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showviz = 0;
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[signal,decision]=obj.equalize(X.signal, D.signal,obj.mu_dd,obj.epochs_dd,n,training);
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% Output Signal
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if obj.decide
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X.signal = decision;
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else
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X.signal = signal;
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end
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X.fs = D.fs; %change sampling frequency of outgoing signal from fdac e.g. 2 sps to symbol spaced = fsym
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lbdesc = [num2str(obj.order),' tap FFE'];
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X = X.logbookentry(lbdesc); % append to logbook
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Noi = X;
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Noi = X - D;
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end
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function [y, d_hat] = equalize(obj, x, d, mio, epochs, N, training)
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||||
|
||||
arguments
|
||||
obj
|
||||
x
|
||||
d
|
||||
mio
|
||||
epochs
|
||||
N
|
||||
training
|
||||
end
|
||||
|
||||
% Initialize Kalman filter variables
|
||||
A = 0.7; % State transition matrix
|
||||
H = 1; % Observation matrix
|
||||
Q = 1e-2; % Process noise covariance
|
||||
R = 1e-3; % Measurement noise covariance
|
||||
P = 1; % Initial error covariance
|
||||
mpi_est = 0; % Initial estimate for MPI noise
|
||||
K = 0; % Kalman gain
|
||||
|
||||
% Error buffers for parallelized structure
|
||||
parallel_depth = 100; % Depending on your parallelization depth
|
||||
err_buffer = zeros(parallel_depth, 1); % Store collected errors
|
||||
mpi_est_buffer = zeros(parallel_depth, 1); % Store MPI estimates
|
||||
|
||||
x = [zeros(floor(obj.order/2), 1); x; zeros(obj.order, 1)];
|
||||
|
||||
for epoch = 1 : epochs
|
||||
symbol = 0;
|
||||
|
||||
for sample = 1 : obj.sps : N
|
||||
symbol = symbol + 1;
|
||||
|
||||
U = x(obj.order + sample - 1 : -1 : sample); % Input window for FFE
|
||||
|
||||
% Subtract the estimated MPI noise from the input signal before FFE
|
||||
x_mpi_reduced = U;
|
||||
y(symbol, 1) = obj.e.' * x_mpi_reduced; % Output of FFE
|
||||
|
||||
y(symbol, 1) = y(symbol, 1) - mpi_est;
|
||||
|
||||
% Decision and error calculation
|
||||
if training
|
||||
[~, symbol_idx] = min(abs(d(symbol) - obj.constellation)); % Closest constellation point
|
||||
d_hat(symbol, 1) = d(symbol);
|
||||
else
|
||||
[~, symbol_idx] = min(abs(y(symbol) - obj.constellation)); % Closest constellation point
|
||||
d_hat(symbol, 1) = obj.constellation(symbol_idx);
|
||||
end
|
||||
|
||||
% Calculate the residual error
|
||||
err(symbol) = y(symbol) - d_hat(symbol); % Instantaneous residual error
|
||||
|
||||
% Collect error for feedback after 'parallel_depth' samples
|
||||
err_buffer(mod(symbol, parallel_depth) + 1) = err(symbol);
|
||||
|
||||
% Update Kalman filter based on the accumulated errors every parallel_depth samples
|
||||
if mod(symbol, parallel_depth) == 0
|
||||
% Compute the mean error over the last 'parallel_depth' samples
|
||||
avg_error = mean(err_buffer);
|
||||
|
||||
% Prediction step (Kalman filter)
|
||||
P = A * P * A' + Q; % Update the error covariance
|
||||
K = P * H' / (H * P * H' + R); % Compute Kalman gain
|
||||
|
||||
% Update MPI noise estimate
|
||||
mpi_est = mpi_est + K * (avg_error - H * mpi_est); % Update based on averaged error
|
||||
P = (1 - K * H) * P; % Update error covariance
|
||||
|
||||
end
|
||||
|
||||
% Store MPI estimate for visualization or debugging
|
||||
k_buffer(symbol) = K;
|
||||
mpi_est_buffer(symbol) = mpi_est;
|
||||
|
||||
% FFE weight update using NLMS
|
||||
if mio ~= 0
|
||||
obj.e = obj.e - (mio * err(symbol) * U); % LMS weight update
|
||||
else
|
||||
normalizationfactor = (U.' * U);
|
||||
obj.e = obj.e - err(symbol) * U / normalizationfactor; % NLMS weight update
|
||||
end
|
||||
|
||||
end
|
||||
end
|
||||
|
||||
if ~training
|
||||
|
||||
figure(111);
|
||||
|
||||
subplot(3,1,1)
|
||||
hold on
|
||||
cla
|
||||
scatter(1:numel(y),y,1,'.');
|
||||
plot(1:numel(mpi_est_buffer), mpi_est_buffer,'DisplayName','mpi est');
|
||||
|
||||
end
|
||||
end
|
||||
|
||||
end
|
||||
|
||||
end
|
||||
|
||||
|
||||
@@ -55,7 +55,7 @@ classdef FFE_adaptive_decision < handle
|
||||
|
||||
end
|
||||
|
||||
function [X] = process(obj, X, D)
|
||||
function [X,Noi] = process(obj, X, D)
|
||||
|
||||
% actual processing of the signal (steps 1. - 3.)
|
||||
% 1 normalize RMS
|
||||
@@ -80,10 +80,13 @@ classdef FFE_adaptive_decision < handle
|
||||
else
|
||||
X.signal = signal;
|
||||
end
|
||||
|
||||
X.fs = D.fs; %change sampling frequency of outgoing signal from fdac e.g. 2 sps to symbol spaced = fsym
|
||||
lbdesc = [num2str(obj.order),' tap FFE'];
|
||||
X = X.logbookentry(lbdesc); % append to logbook
|
||||
|
||||
Noi = X;
|
||||
Noi = X - D;
|
||||
|
||||
end
|
||||
|
||||
@@ -124,12 +127,15 @@ classdef FFE_adaptive_decision < handle
|
||||
d_hat(symbol,1) = obj.constellation(symbol_idx);
|
||||
end
|
||||
|
||||
|
||||
y_buffer(symbol_idx,1) = y(symbol);
|
||||
y_buffer(symbol_idx,:) = circshift(y_buffer(symbol_idx,:),1);
|
||||
|
||||
adap_constellation = mean(y_buffer,2,"omitnan");
|
||||
delta_y_y(symbol) = y(symbol) - adap_constellation(symbol_idx);
|
||||
|
||||
err(symbol) = y(symbol) - d_hat(symbol); % Instantaneous error
|
||||
|
||||
|
||||
if mio ~= 0
|
||||
obj.e = obj.e - (mio * err(symbol) * U) ; % Weight update rule of LMS
|
||||
else
|
||||
@@ -137,7 +143,6 @@ classdef FFE_adaptive_decision < handle
|
||||
obj.e = obj.e - err(symbol) * U / normalizationfactor; % Weight update rule of NLMS
|
||||
end
|
||||
|
||||
|
||||
obj.error(epoch,symbol) = err(symbol) * err(symbol)'; % Instantaneous square error
|
||||
|
||||
end
|
||||
|
||||
@@ -2,16 +2,16 @@
|
||||
%% Parameter to simulate and save
|
||||
params = struct;
|
||||
|
||||
params.M = [6];
|
||||
params.datarate = [448];
|
||||
params.rop = [0];
|
||||
params.M = [4];
|
||||
params.datarate = [224];
|
||||
params.rop = [-12:-5];
|
||||
|
||||
precomp_mode = 0; %0=do nothing ; 1= measure; 2=precomp active
|
||||
postfilter = 1; % noise whiten. approach -> Postfilter + MLSE
|
||||
postfilter = 0; % noise whiten. approach -> Postfilter + MLSE
|
||||
|
||||
db_precode = 0;
|
||||
db_precode = 1;
|
||||
db_encode = 0;
|
||||
db_channelapproach = 0;
|
||||
db_channelapproach = 1;
|
||||
|
||||
|
||||
if ismac
|
||||
@@ -125,7 +125,7 @@ for M = wh.parameter.M.values
|
||||
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);
|
||||
|
||||
% Receiver ROP curve
|
||||
for i = 1:i_
|
||||
parfor i = 1:i_
|
||||
rop=wh.parameter.rop.values(i);
|
||||
|
||||
% Set ROP
|
||||
|
||||
247
projects/400G_FTN_setups/imdd_dsp_lab_copy_200G.m
Normal file
247
projects/400G_FTN_setups/imdd_dsp_lab_copy_200G.m
Normal file
@@ -0,0 +1,247 @@
|
||||
|
||||
%% Parameter to simulate and save
|
||||
params = struct;
|
||||
|
||||
params.M = [4];
|
||||
params.datarate = [184];
|
||||
params.rop = [-12:-5];
|
||||
|
||||
precomp_mode = 0; %0=do nothing ; 1= measure; 2=precomp active
|
||||
postfilter = 0; % noise whiten. approach -> Postfilter + MLSE
|
||||
|
||||
db_precode = 0;
|
||||
db_encode = 0;
|
||||
db_channelapproach = 0;
|
||||
|
||||
if ismac
|
||||
precomp_path = "/Users/silasoettinghaus/Documents/MATLAB/imdd_simulation/projects/standard_system";
|
||||
else
|
||||
precomp_path = "C:\Users\sioe\Documents\MATLAB\imdd_simulation\projects\standard_system\";
|
||||
end
|
||||
|
||||
precomp_fn = "400G_simulative_setup";
|
||||
usemrds = 0;
|
||||
|
||||
name = ['wh_',strrep(num2str(now),'.','')];
|
||||
|
||||
wh = DataStorage(params);
|
||||
|
||||
wh.addStorage("ber_ffe");
|
||||
|
||||
%% Init Params
|
||||
link_length = 10000; %meter
|
||||
pn_key = 2;
|
||||
laser_linewidth = 0;
|
||||
|
||||
endcnt = prod(wh.dim);
|
||||
cnt=0;
|
||||
|
||||
disp(['Start Simulation of ',num2str(endcnt),' loops...'])
|
||||
tic
|
||||
|
||||
for M = wh.parameter.M.values
|
||||
for datarate = wh.parameter.datarate.values
|
||||
|
||||
% SETUP HERE: %%
|
||||
kover = 16;
|
||||
Awg = M8196A("kover",kover);
|
||||
fdac = Awg.fdac;
|
||||
fsym = round(datarate / log2(M)) * 1e9;
|
||||
rrcalpha = 0.05;
|
||||
Pform = Pulseformer("fsym",fsym,"fdac",4*fsym,"pulse","rrc","pulselength",16,"rrcalpha",rrcalpha);
|
||||
|
||||
% MAIN SIGNAL
|
||||
|
||||
%%%%% Symbol Generation %%%%%%
|
||||
[Digi_sig,Symbols,Bits] = PAMsource("fsym",fsym,"M",M,"order",18,"useprbs",0,...
|
||||
"fs_out",Awg.fdac,"applyclipping",1,"clipfactor",1.5,...
|
||||
"applypulseform",0,"pulseformer",Pform,"randkey",pn_key,...
|
||||
"db_precode",db_precode,"db_encode",db_encode,...
|
||||
"mrds_code",usemrds,"mrds_blocklength",512).process();
|
||||
|
||||
% Digi_sig.eye(fsym,M);
|
||||
Digi_sig.spectrum("fignum",123434,"displayname",'Digital Tx Signal');
|
||||
|
||||
if precomp_mode == 1
|
||||
freqresp = ChannelFreqResp("Nacq",1024,"Navg",64,"Ncp",63,'f_ref',Digi_sig.fs);
|
||||
Digi_sig = freqresp.buildOFDM();
|
||||
elseif precomp_mode == 2
|
||||
Digi_sig = ChannelFreqResp("Nacq",1024,"Navg",64,"Ncp",63,'f_ref',Digi_sig.fs).precomp(Digi_sig,'maxampdb',1,'loadPath',precomp_path,'fileName',precomp_fn);
|
||||
Digi_sig.spectrum("fignum",11,"displayname",'after precomp');
|
||||
end
|
||||
|
||||
%%%%% AWG %%%%%%
|
||||
El_sig = Awg.process(Digi_sig);
|
||||
|
||||
% El_sig.spectrum("displayname",'el','fignum',123434);
|
||||
|
||||
% El_sig.signal = awgn(El_sig.signal,-3,'measured',pn_key);
|
||||
|
||||
%%%%% Lowpass el. components %%%%%%
|
||||
El_sig = Filter('filtdegree',2,"f_cutoff",60e9,"fs",fdac*kover,"filterType",filtertypes.butterworth,"active",true).process(El_sig);
|
||||
|
||||
%%%%% Electrical Driver Amplifier %%%%%%
|
||||
El_sig = Amplifier("amp_mode","ideal_no_noise","gain_mode","gain","amplification_db",3).process(El_sig);
|
||||
% El_sig = El_sig.setPower(6,"dBm");
|
||||
|
||||
fprintf('Driver output power: %s dBm\n', num2str(El_sig.power));
|
||||
fprintf('Driver output peak voltage: %s Vpp \n', num2str(max(El_sig.signal)-min(El_sig.signal)));
|
||||
|
||||
|
||||
% MAIN SIGNAL
|
||||
%%%%% MODULATE E/O CONVERSION %%%%%%
|
||||
vbias_rel = 0.5;
|
||||
u_pi = 2.9;
|
||||
vbias = -vbias_rel*u_pi;
|
||||
|
||||
[Opt_sig] = EML("mode",eml_mode.im_cosinus,"power",3,"fsimu",El_sig.fs,"lambda",1290,"bias",vbias,"u_pi",u_pi,"linewidth",laser_linewidth,"randomkey",pn_key).process(El_sig);
|
||||
|
||||
% Opt_sig.eye(fsym,7);
|
||||
%
|
||||
% figure(10)
|
||||
% hold on
|
||||
% scatter(El_sig.signal(1:100000)+vbias,(abs(Opt_sig.signal(1:100000)).^2)*1e3,0.1,'.','DisplayName','Modulator TF')
|
||||
% ylim([0 4]);
|
||||
% xlim([-u_pi/2, u_pi/2]+vbias);
|
||||
% xlabel('Input in V')
|
||||
% ylabel('abs(Output) in mW')
|
||||
|
||||
Optfilter = Filter('filtdegree',6,"f_cutoff",fsym.*0.7,"fs",fdac*kover,"filterType",filtertypes.gaussian,"active",true);
|
||||
Opt_sig = Optfilter.process(Opt_sig);
|
||||
|
||||
% Opt_sig.spectrum("fignum",122,"displayname",['Tx SPectrum; PAM ',num2str(M)]);
|
||||
|
||||
Opt_sig = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power","amplification_db",0).process(Opt_sig);
|
||||
|
||||
i_ = wh.parameter.rop.length;
|
||||
|
||||
ber_ffe=zeros(i_);
|
||||
|
||||
patten=zeros(i_);
|
||||
|
||||
%%%%% 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);
|
||||
|
||||
% Receiver ROP curve
|
||||
parfor i = 1:i_
|
||||
|
||||
rop=wh.parameter.rop.values(i);
|
||||
|
||||
% Set ROP
|
||||
Rx_sig = Amplifier("amp_mode","ideal_no_noise","gain_mode","output_power","amplification_db",rop).process(Opt_sig);
|
||||
patten(i) = Rx_sig.power;
|
||||
|
||||
%%%%%% Square Law %%%%%%
|
||||
Rx_sig = Photodiode("fsimu",fdac*kover,"dark_current",2e-08,"responsivity",1,"temperature",20,"nep",1.8e-11).process(Rx_sig);
|
||||
|
||||
%%%%%% Lowpass PhDiode %%%%%%
|
||||
Rx_sig = Filter('filtdegree',2,"f_cutoff",70e9,"fs",fdac*kover,"filterType",filtertypes.gaussian,"active",true).process(Rx_sig);
|
||||
|
||||
%%%%%% Scope %%%%%%
|
||||
fadc = 160e9;
|
||||
Lp_scpe = Filter('filtdegree',4,"f_cutoff",63e9,"fs",fadc,"filterType",filtertypes.butterworth,"active",true);
|
||||
Scpe_sig = Scope("fsimu",fdac*kover,"fadc",fadc,...
|
||||
"delay",0,"fixed_delay",0,"filtertype",filtertypes.butterworth,...
|
||||
"samplingdelay",0,"rand_samplingdelay",0,"freq_offset",0,"samp_jitter",0,...
|
||||
"adcresolution",5.5,"quantbuffer",0.1,'block_dc',1,'lpf_active',1,'H_lpf',Lp_scpe).process(Rx_sig);
|
||||
|
||||
if precomp_mode == 1
|
||||
freqresp.estimate(Scpe_sig,"save",true,"savePath",precomp_path,"fileName",precomp_fn);
|
||||
freqresp.plot();
|
||||
end
|
||||
|
||||
Scpe_sig.spectrum("displayname",'After Scope','fignum',123434);
|
||||
|
||||
%%%%%% Sample to 2x fsym %%%%%%
|
||||
Scpe_sig = Scpe_sig.resample("fs_in",fadc,"fs_out",2*fsym);
|
||||
|
||||
%%%%%% Sync Rx signal with reference %%%%%%
|
||||
[Scpe_sig,S] = Scpe_sig.tsynch("reference",Symbols,"fs_ref",fsym);
|
||||
|
||||
%%%%% EQUALIZE %%%%%%
|
||||
Eq = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",25,"sps",2,"decide",0);
|
||||
%Eq = VNLE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",[0.0004 0.0005 0.0006],"mu_tr",0,"order",[50,7,7],"sps",2,"decide",1);
|
||||
|
||||
if db_channelapproach
|
||||
% ref symbols and transm. sequence are precoded
|
||||
[EQ_sig, Noi] = Eq.process(Scpe_sig,Duobinary().encode(Symbols));
|
||||
else
|
||||
[EQ_sig, Noi] = Eq.process(Scpe_sig,Symbols);
|
||||
end
|
||||
|
||||
if db_encode || db_channelapproach
|
||||
EQ_sig = MLSE("DIR",[1,1],"duobinary_output",1,"M",M,"trellis_states",PAMmapper(M,0).levels).process(EQ_sig);
|
||||
EQ_sig = Duobinary().decode(EQ_sig);
|
||||
end
|
||||
|
||||
if postfilter
|
||||
% Noi.spectrum("displayname",'Noise Spectrum','fignum',1234);
|
||||
% EQ_sig.spectrum("displayname","Signal Spectrum","fignum",1234);
|
||||
|
||||
nc = 2;
|
||||
burg_coeff = arburg(Noi.signal,nc);
|
||||
|
||||
EQ_sig = EQ_sig.filter(burg_coeff,1);
|
||||
|
||||
% EQ_sig.spectrum("displayname","Signal Spectrum after Postfilter","fignum",1234);
|
||||
tic
|
||||
EQ_sig = MLSE("DIR",burg_coeff,"duobinary_output",0,"M",M,"trellis_states",PAMmapper(M,0).levels).process(EQ_sig);
|
||||
toc
|
||||
% EQ_sig.spectrum("displayname","Signal Spectrum after MLSE","fignum",1234);
|
||||
|
||||
if 1
|
||||
Noi.spectrum('displayname','Noise PSD','fignum',123)
|
||||
[h,w] = freqz(1,burg_coeff,length(Noi),"whole",Noi.fs);
|
||||
h = h/max(abs(h));
|
||||
hold on
|
||||
w_ = (w - Noi.fs/2);
|
||||
plot(w_.*1e-9,20*log10(fftshift(h)),'DisplayName',['', num2str(nc), ' coefficients for burg alg.']);
|
||||
end
|
||||
end
|
||||
|
||||
Rx_bits = PAMmapper(M,0).demap(EQ_sig);
|
||||
[~,errors_bm,ber_ffe(i),errors] = calc_ber(Rx_bits.signal,Bits.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
|
||||
disp(['BER: ',sprintf('%.1E',ber_ffe(i)),' - - ROP: ',num2str(patten(i)),'dBm - - PAM-',num2str(M),' - - ',num2str(fsym*1e-9),' GBd']);
|
||||
|
||||
end
|
||||
|
||||
for i = 1:i_
|
||||
rop=wh.parameter.rop.values(i);
|
||||
|
||||
wh.addValueToStorage(ber_ffe(i),'ber_ffe',M,datarate,rop);
|
||||
|
||||
end
|
||||
|
||||
toc
|
||||
|
||||
% wh.save('C:\Users\Silas\Documents\MATLAB\imdd_simulation\projects\MPI_August\auswertung\')
|
||||
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
disp('Simulation Done!')
|
||||
|
||||
|
||||
cols = linspecer(8);
|
||||
|
||||
%cnt = cnt+1;
|
||||
ber_ffe = wh.getStoValue('ber_ffe',M,datarate,wh.parameter.rop.values);
|
||||
|
||||
% Create the initial plot
|
||||
|
||||
figure(44);
|
||||
a = gca;
|
||||
hold on; % Retain the plot so new points can be added without complete redraw
|
||||
|
||||
plot(wh.parameter.rop.values,ber_ffe,"LineWidth",0.5,"LineStyle","-","Marker",".","MarkerSize",15,"DisplayName","FFE only");
|
||||
yline(3.8e-3,'DisplayName','HD-FEC','LineStyle','--','HandleVisibility','off');
|
||||
xlabel('Received Optical Power (dBm)');
|
||||
ylabel('Bit Error Rate (BER)');
|
||||
title('Bit Error Rate vs. ROP');
|
||||
set(gca,'yscale','log');
|
||||
set(gca,'Box','on');
|
||||
grid on;
|
||||
grid minor
|
||||
legend
|
||||
@@ -2,21 +2,21 @@
|
||||
%% Parameter to simulate and save
|
||||
params = struct;
|
||||
|
||||
params.M = [6];
|
||||
params.datarate = [448];
|
||||
params.M = [4];
|
||||
params.datarate = [300];
|
||||
params.rop = [0];
|
||||
|
||||
precomp_mode = 0; %0=do nothing ; 1= measure; 2=precomp active
|
||||
postfilter = 1; % noise whiten. approach -> Postfilter + MLSE
|
||||
postfilter = 0; % noise whiten. approach -> Postfilter + MLSE
|
||||
|
||||
db_precode = 0;
|
||||
db_encode = 0;
|
||||
db_channelapproach = 0;
|
||||
|
||||
laser_linewidth = 1e6;
|
||||
laser_linewidth = 5e6;
|
||||
random_key_sequence = 2;
|
||||
random_key_laser_phase = 4;
|
||||
sir = 25;
|
||||
random_key_laser_phase = 11;
|
||||
sir = 20;
|
||||
|
||||
if ismac
|
||||
precomp_path = "/Users/silasoettinghaus/Documents/MATLAB/imdd_simulation/projects/standard_system";
|
||||
@@ -25,6 +25,7 @@ else
|
||||
end
|
||||
|
||||
precomp_fn = "400G_simulative_setup";
|
||||
|
||||
usemrds = 0;
|
||||
|
||||
name = ['wh_',strrep(num2str(now),'.','')];
|
||||
@@ -56,7 +57,7 @@ for M = wh.parameter.M.values
|
||||
% MAIN SIGNAL
|
||||
|
||||
%%%%% Symbol Generation MAIN %%%%%%
|
||||
[Digi_sig,Symbols,Bits] = PAMsource("fsym",fsym,"M",M,"order",18,"useprbs",0,...
|
||||
[Digi_sig,Symbols,Bits] = PAMsource("fsym",fsym,"M",M,"order",18,"useprbs",1,...
|
||||
"fs_out",M8199.fdac,"applyclipping",1,"clipfactor",1.5,...
|
||||
"applypulseform",0,"pulseformer",Pform,"randkey",random_key_sequence,...
|
||||
"db_precode",db_precode,"db_encode",db_encode,...
|
||||
@@ -171,7 +172,7 @@ for M = wh.parameter.M.values
|
||||
freqresp.plot();
|
||||
end
|
||||
|
||||
Scpe_sig.spectrum("displayname",'After Scope','fignum',123434);
|
||||
%Scpe_sig.spectrum("displayname",'After Scope','fignum',123434);
|
||||
|
||||
%%%%%% Sample to 2x fsym %%%%%%
|
||||
Scpe_sig = Scpe_sig.resample("fs_in",fadc,"fs_out",2*fsym);
|
||||
@@ -183,21 +184,31 @@ for M = wh.parameter.M.values
|
||||
Eq = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",25,"sps",2,"decide",0);
|
||||
%Eq = VNLE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",[0.0004 0.0005 0.0006],"mu_tr",0,"order",[50,7,7],"sps",2,"decide",1);
|
||||
|
||||
Eq = EQ("Ne",[50,7,7],"Nb",[0,0,0],"training_length",4096*2,"training_loops",5,"dd_loops",5,"K",2,"DCmu",0.05,"DDmu",[0.0004 0.0004 0.0004 0.0004 ],"DFEmu",0.005,"FFEmu",0,"plotfinal",0,"ideal_dfe",1);
|
||||
|
||||
Eq = FFE_Kalman("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",25,"sps",2,"decide",0);
|
||||
|
||||
% Eq = FFE_Kalman_Feedback("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",25,"sps",2,"decide",0);
|
||||
|
||||
% 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",80);
|
||||
|
||||
Eq = FFE_DCremoval("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",25,"sps",2,"decide",0,"mu_dc",0.05,"dc_buffer_len",100);
|
||||
|
||||
if db_channelapproach
|
||||
% ref symbols and transm. sequence are precoded
|
||||
[EQ_sig, Noi] = Eq.process(Scpe_sig,Duobinary().encode(Symbols));
|
||||
else
|
||||
[EQ_sig, Noi] = Eq.process(Scpe_sig,Symbols);
|
||||
end
|
||||
|
||||
if db_encode || db_channelapproach
|
||||
EQ_sig = MLSE("DIR",[1,1],"duobinary_output",1,"M",M,"trellis_states",PAMmapper(M,0).levels).process(EQ_sig);
|
||||
EQ_sig = Duobinary().decode(EQ_sig);
|
||||
end
|
||||
|
||||
if postfilter
|
||||
% Noi.spectrum("displayname",'Noise Spectrum','fignum',1234);
|
||||
% EQ_sig.spectrum("displayname","Signal Spectrum","fignum",1234);
|
||||
elseif db_encode
|
||||
|
||||
[EQ_sig, Noi] = Eq.process(Scpe_sig,Symbols);
|
||||
EQ_sig = MLSE("DIR",[1,1],"duobinary_output",1,"M",M,"trellis_states",PAMmapper(M,0).levels).process(EQ_sig);
|
||||
EQ_sig = Duobinary().decode(EQ_sig);
|
||||
|
||||
elseif postfilter
|
||||
|
||||
[EQ_sig, Noi] = Eq.process(Scpe_sig,Symbols);
|
||||
|
||||
nc = 2;
|
||||
burg_coeff = arburg(Noi.signal,nc);
|
||||
@@ -218,12 +229,24 @@ for M = wh.parameter.M.values
|
||||
w_ = (w - Noi.fs/2);
|
||||
plot(w_.*1e-9,20*log10(fftshift(h)),'DisplayName',['', num2str(nc), ' coefficients for burg alg.']);
|
||||
end
|
||||
|
||||
|
||||
else
|
||||
|
||||
[EQ_sig, Noi] = Eq.process(Scpe_sig,Symbols);
|
||||
|
||||
if 0
|
||||
Noi.spectrum('displayname','Noise PSD','fignum',123)
|
||||
EQ_sig.plot("displayname",'After EQ','fignum',1112);
|
||||
end
|
||||
end
|
||||
|
||||
Rx_bits = PAMmapper(M,0).demap(EQ_sig);
|
||||
[~,errors_bm,ber_ffe(i),errors] = calc_ber(Rx_bits.signal,Bits.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
|
||||
disp(['BER: ',sprintf('%.1E',ber_ffe(i)),' - - ROP: ',num2str(patten(i)),'dBm - - PAM-',num2str(M),' - - ',num2str(fsym*1e-9),' GBd']);
|
||||
|
||||
|
||||
|
||||
end
|
||||
|
||||
for i = 1:i_
|
||||
@@ -255,7 +278,9 @@ figure(44);
|
||||
a = gca;
|
||||
hold on; % Retain the plot so new points can be added without complete redraw
|
||||
|
||||
plot(wh.parameter.rop.values,ber_ffe,"LineWidth",0.5,"LineStyle","-","Marker",".","MarkerSize",15,"DisplayName","FFE only");
|
||||
dispname = ['Linewidth: ',num2str(laser_linewidth.*1e-6),' MHz'];
|
||||
|
||||
plot(wh.parameter.rop.values,ber_ffe,"LineWidth",0.5,"LineStyle","-","Marker",".","MarkerSize",15,"DisplayName",dispname);
|
||||
yline(3.8e-3,'DisplayName','HD-FEC','LineStyle','--','HandleVisibility','off');
|
||||
xlabel('Received Optical Power (dBm)');
|
||||
ylabel('Bit Error Rate (BER)');
|
||||
|
||||
@@ -5,14 +5,15 @@ params = struct;
|
||||
params.M = [4];
|
||||
params.datarate = [224];
|
||||
params.sir = [35]; %decibel = attenuation of interference path
|
||||
params.laser_linewidth = [10e6];
|
||||
params.laser_linewidth = [1e6];
|
||||
|
||||
params.pn_key = [1];
|
||||
params.rop = [-12:1:-1];
|
||||
|
||||
params.rop = 0;
|
||||
|
||||
|
||||
usemrds = 0;
|
||||
wl = 512;
|
||||
|
||||
name = ['wh_',strrep(num2str(now),'.','')];
|
||||
|
||||
@@ -144,7 +145,8 @@ for M = wh.parameter.M.values
|
||||
|
||||
% Receiver ROP curve
|
||||
|
||||
parfor i = 1:i_
|
||||
for i = 1:i_
|
||||
|
||||
rop=wh.parameter.rop.values(i);
|
||||
|
||||
% Set ROP
|
||||
@@ -234,33 +236,43 @@ for M = wh.parameter.M.values
|
||||
% Scpe_sig.plot("fignum",313,"displayname",'after dc removal');
|
||||
|
||||
%%%%%% Sync Rx signal with reference %%%%%%
|
||||
[Scpe_sig,D,cuts] = Scpe_sig.tsynch("reference",Symbols,"fs_ref",fsym);
|
||||
[Scpe_sig] = Scpe_sig.tsynch("reference",Symbols,"fs_ref",fsym);
|
||||
|
||||
|
||||
%%%%% EQUALIZE %%%%%%
|
||||
|
||||
if ~usemrds
|
||||
%
|
||||
Eq = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",25,"sps",2,"decide",1);
|
||||
Eq = FFE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",25,"sps",2,"decide",0);
|
||||
% Eq = VNLE("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",[0.0004 0.0005 0.0006],"mu_tr",0,"order",[25,2,2],"sps",2,"decide",1);
|
||||
[EQ_sig] = Eq.process(Scpe_sig,Symbols);
|
||||
[EQ_sig,Noi] = Eq.process(Scpe_sig,Symbols);
|
||||
Rx_bits = PAMmapper(M,0).demap(EQ_sig);
|
||||
[~,errors_bm,ber_ffe(j,i),errors] = calc_ber(Rx_bits.signal,Bits.signal,"skip_front",100,"skip_end",150,"returnErrorLocation",1);
|
||||
disp(['BER: ',sprintf('%.1E',ber_ffe(j,i)),' - - ROP: ',num2str(patten(j,i)),'dBm - - PAM-',num2str(M),' - - ',num2str(fsym*1e-9),' GBd']);
|
||||
%
|
||||
% %
|
||||
|
||||
|
||||
% Eq = FFE_FFDCAVG("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",25,"sps",2,"decide",1,"mu_buff",0.7);
|
||||
% [EQ_sig] = Eq.process(Scpe_sig,Symbols);
|
||||
% Rx_bits = PAMmapper(M,0).demap(EQ_sig);
|
||||
% [~,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']);
|
||||
%
|
||||
%
|
||||
% 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",112);
|
||||
% [EQ_sig] = Eq.process(Scpe_sig,Symbols);
|
||||
% 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);
|
||||
% disp(['BER: ',sprintf('%.1E',ber_adapt(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",0,"buffer_length",112);
|
||||
[EQ_sig,Noi] = Eq.process(Scpe_sig,Symbols);
|
||||
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);
|
||||
disp(['BER: ',sprintf('%.1E',ber_adapt(j,i)),' - - ROP: ',num2str(patten(j,i)),'dBm - - PAM-',num2str(M),' - - ',num2str(fsym*1e-9),' GBd']);
|
||||
|
||||
Noi.spectrum('displayname','Noise PSD','fignum',123)
|
||||
[h,w] = freqz(1,burg_coeff,length(Noi),"whole",Noi.fs);
|
||||
h = h/max(abs(h));
|
||||
hold on
|
||||
w_ = (w - Noi.fs/2);
|
||||
plot(w_.*1e-9,20*log10(fftshift(h)),'DisplayName',['', num2str(nc), ' coefficients for burg alg.']);
|
||||
|
||||
%
|
||||
%
|
||||
% Eq = FFE_DCremoval("epochs_tr",5,"epochs_dd",5,"len_tr",4096*2,"mu_dd",1e-4,"mu_tr",0,"order",25,"sps",2,"decide",1,"mu_dc",0.07,"dc_buffer_len",112);
|
||||
|
||||
Reference in New Issue
Block a user