EQ Structures for MPI mitigation
This commit is contained in:
Silas
179
Classes/04_DSP/FFE_DCremoval.m
Normal file
179
Classes/04_DSP/FFE_DCremoval.m
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@@ -0,0 +1,179 @@
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classdef FFE_DCremoval < 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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mu_dc
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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_DCremoval(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.mu_dc = 0.05;
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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] = 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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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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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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err = 0;
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e_dc = 0;
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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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symbol = symbol+1;
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U = x(obj.order+sample-1:-1:sample);
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y(symbol,1) = e_dc + obj.e.' * U; % Calculating output of LMS __ * |
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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)); % decision for 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 error
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if mio ~= 0
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obj.e = obj.e - (mio * err(symbol) * U) ; % Weight update rule of LMS
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else
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normalizationfactor = (U.' * U);
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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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e_dc = e_dc - obj.mu_dc * err(symbol);
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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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end
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end
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end
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193
Classes/04_DSP/FFE_FFDCAVG.m
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193
Classes/04_DSP/FFE_FFDCAVG.m
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@@ -0,0 +1,193 @@
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classdef FFE_FFDCAVG < 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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mu_buff
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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_FFDCAVG(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.mu_buff = 0;
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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] = 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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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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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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err_buffer = zeros(numel(obj.constellation),50);
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for sample = 1 : obj.sps : N
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symbol = symbol+1;
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U = x(obj.order+sample-1:-1:sample);
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y(symbol,1) = obj.e.' * U; % Calculating output of LMS __ * |
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if training
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[~,symbol_idx] = min(abs(d(symbol) - obj.constellation)); % decision for closest constellation point
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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)); % decision for 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 error
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err_buffer(symbol_idx,1) = err(symbol);
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err_buffer(symbol_idx,:) = circshift(err_buffer(symbol_idx,:),1);
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y(symbol) = y(symbol) - obj.mu_buff*mean(err_buffer(symbol_idx,:));
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if training
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[~,symbol_idx] = min(abs(d(symbol) - obj.constellation)); % decision for closest constellation point
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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)); % decision for 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 error
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if mio ~= 0
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obj.e = obj.e - (mio * err(symbol) * U) ; % Weight update rule of LMS
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else
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normalizationfactor = (U.' * U);
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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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end
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end
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end
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end
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end
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149
Classes/04_DSP/FFE_adaptive_decision.m
Normal file
149
Classes/04_DSP/FFE_adaptive_decision.m
Normal file
@@ -0,0 +1,149 @@
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classdef FFE_adaptive_decision < 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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buffer_length
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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_adaptive_decision(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.buffer_length = 100;
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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] = process(obj, X, D)
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% actual processing of the signal (steps 1. - 3.)
|
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% 1 normalize RMS
|
||||
X = X.normalize("mode","rms");
|
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|
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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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||||
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||||
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||||
end
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||||
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function [y,d_hat] = equalize(obj,x,d,mio,epochs,N,training,showviz)
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|
||||
arguments
|
||||
obj
|
||||
x
|
||||
d
|
||||
mio
|
||||
epochs
|
||||
N
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||||
training
|
||||
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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% y_buffer = zeros(numel(obj.constellation),500);
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y_buffer = repmat(obj.constellation,1,obj.buffer_length);
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for sample = 1 : obj.sps : N
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symbol = symbol+1;
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U = x(obj.order+sample-1:-1:sample);
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y(symbol,1) = obj.e.' * U; % Calculating output of LMS __ * |
|
||||
|
||||
if training
|
||||
[~,symbol_idx] = min(abs(d(symbol) - obj.constellation)); % decision for closest constellation point
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d_hat(symbol,1) = d(symbol);
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else
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y_buffer(y_buffer==0) = NaN;
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adap_constellation = mean(y_buffer,2,"omitnan");
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[~,symbol_idx] = min(abs(y(symbol) - adap_constellation)); % decision for closest constellation point
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d_hat(symbol,1) = obj.constellation(symbol_idx);
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end
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y_buffer(symbol_idx,1) = y(symbol);
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y_buffer(symbol_idx,:) = circshift(y_buffer(symbol_idx,:),1);
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err(symbol) = y(symbol) - d_hat(symbol); % Instantaneous error
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||||
|
||||
if mio ~= 0
|
||||
obj.e = obj.e - (mio * err(symbol) * U) ; % Weight update rule of LMS
|
||||
else
|
||||
normalizationfactor = (U.' * U);
|
||||
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
|
||||
end
|
||||
|
||||
end
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||||
|
||||
end
|
||||
end
|
||||
|
||||
Reference in New Issue
Block a user