70fd0a44be
Better plot in channel freq response imdd_mpi simulation is a good 400G model DSP offline analysis script
327 lines
11 KiB
Matlab
327 lines
11 KiB
Matlab
classdef PAMmapper
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%PAMMAPPER Summary of this class goes here
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% Detailed explanation goes here
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properties
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M
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unipolar
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thresholds
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levels
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scaling
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end
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methods
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function obj = PAMmapper(M, unipolar)
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%PAMMAPPER Construct an instance of this class
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% Detailed explanation goes here
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obj.M = M;
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obj.unipolar = unipolar;
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obj.thresholds = obj.get_demodulation_thresholds();
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obj.levels = obj.get_levels();
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obj.scaling = rms(obj.get_levels());
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end
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function out = map(obj,signal_in)
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if isa(signal_in,'Signal')
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signal_in.signal = obj.map_(signal_in.signal);
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% signal_in = signal_in.normalize("mode","rms");
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lbdesc = ['Map bat stream to PAM ',num2str(obj.M),' symbols'];
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signal_in = signal_in.logbookentry(lbdesc,obj);
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out = signal_in;
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else
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out = signal_in;
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end
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end
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function signalclass_out = demap(obj,signalclass_in)
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signalclass_in.signal = obj.demap_(signalclass_in.signal);
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lbdesc = ['Demap PAM ',num2str(obj.M),' symbols to bit stream'];
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signalclass_in = signalclass_in.logbookentry(lbdesc,obj);
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signalclass_out = signalclass_in;
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end
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function pam_sig = map_(obj,bitpattern)
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switch obj.M
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case 2
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% 2-ASK: BPSK / OOK
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pam_sig=bitpattern(:,1);
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if obj.unipolar==0
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pam_sig=2*pam_sig-1;
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end
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case 4
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% 4-ASK:
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pam_sig=2*bitpattern(:,1)+(bitpattern(:,1)==bitpattern(:,2));
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if obj.unipolar==0
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pam_sig=2*pam_sig-3;
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end
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pam_sig = pam_sig/sqrt(5);
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case 6
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m = 1;
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if size(bitpattern,2)>size(bitpattern,1)
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bitpattern = bitpattern'; %vector aufrecht stellen
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end
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% LUT based mapping
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for k = 1:5:fix(length(bitpattern)/5)*5
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pam_sig(m:m+1,1) = obj.thresholds(bin2dec(int2str(bitpattern(k:k+4)'))+1,:);
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m = m+2;
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end
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pam_sig = pam_sig/sqrt(10);
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case 8
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% 8-ASK:
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x1 = bitpattern(:,1);
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x2 = (bitpattern(:,1)==bitpattern(:,3));
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x3 = x2~=bitpattern(:,2);
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pam_sig = 4*x1 + 2*x2 + x3;
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if obj.unipolar==0
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pam_sig=2*pam_sig-7;
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end
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pam_sig = pam_sig/sqrt(21);
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case 16
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% 16-ASK:
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x1 = bitpattern(:,1);
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x2 = (bitpattern(:,1)==bitpattern(:,4));
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x3 = x2~=bitpattern(:,3);
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x4 = x3~=bitpattern(:,2);
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pam_sig = 8*x1 + 4*x2 + 2*x3 + x4;
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if obj.unipolar==0
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pam_sig=2*pam_sig-15;
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end
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end
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end
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function thres = get_demodulation_thresholds(obj)
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%simply get the obj.thresholdseshold values for PAM
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%28.03.2023 - Silas Oett. - Extracted from digi_demod.m
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%
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% switch obj.M
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% case 2
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% thres = 0;
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% case 4
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% thres = [-2 0 2];
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% case 6
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% thres = [-3 5;-1 5;-3 -5;-1 -5;-5 3;-5 1;-5 -3;-5 -1;-1 3;-1 1;-1 -3;-1 -1;-3 3;-3 1;-3 -3;-3 -1;3 5;1 5;3 -5;1 -5;5 3;5 1;5 -3;5 -1;1 3;1 1;1 -3;1 -1;3 3;3 1;3 -3;3 -1];
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% case 8
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% thres = [-6 -4 -2 0 2 4 6];
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% case 16
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% thres = [-10 -8 -6 -4 -2 0 2 4 6 8 10];
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% end
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switch obj.M
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case 2
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% 2-ASK
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if obj.unipolar
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thres=0.5;
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else %bi polar
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thres=0;
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end
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case 4
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% 4-ASK
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if obj.unipolar==0
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thres=[-2,0,2];
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elseif obj.unipolar==1
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thres=[0.5,1.5,2.5];
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end
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thres = thres .* 1/sqrt(5);
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case 6 %PAM 6
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thres = [-3 5;-1 5;-3 -5;-1 -5;-5 3;-5 1;-5 -3;-5 -1;-1 3;-1 1;-1 -3;-1 -1;-3 3;-3 1;-3 -3;-3 -1;3 5;1 5;3 -5;1 -5;5 3;5 1;5 -3;5 -1;1 3;1 1;1 -3;1 -1;3 3;3 1;3 -3;3 -1];
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case 8
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% 8-ASK
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if obj.unipolar==0
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thres=-6:2:6;
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elseif obj.unipolar==1
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thres=0.5:6.5;
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end
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thres=thres./sqrt(21);
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case 16
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% 16-ASK
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if obj.unipolar==0 && scale_mode==1
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thres=-14:2:14;
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elseif obj.unipolar==1 && scale_mode==1
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thres=0.5:14.5;
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end
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end
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end
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function levels = get_levels(obj)
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switch obj.M
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case 2
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levels = [-1 1];
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case 4
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levels = [-3 -1 1 3];
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case 6
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levels = [-5 -3 -1 1 3 5];
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case 8
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levels = [-7 -5 -3 -1 1 3 5 7];
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case 16
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levels = [-11 -9 -7 -5 -3 -1 1 3 5 7 9 11];
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end
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end
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function [data_out] = demap_(obj,data_in)
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data_in= data_in';
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if obj.M ~= 6
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% create output
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if ~isempty(obj.thresholds)
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a = squeeze(repmat(real(data_in),[1 1 length(obj.thresholds)])); %Eingangssignal in 3 spalten
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b = squeeze(repmat(reshape(obj.thresholds(:).',[1 1 length(obj.thresholds)]),[1 length(data_in) 1])); %Threshold in 3 Spalten
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comp_real = a > b; %check for each symbol/ sampling if it exeeds the obj.thresholdseshold 1, 2 or 3
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comp_real=repmat(real(data_in),[1 1 length(obj.thresholds)]) > repmat(reshape(obj.thresholds(:).',[1 1 length(obj.thresholds)]),[1 length(data_in) 1]);
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else
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comp_real=[];
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end
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s1=size(comp_real,1);
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s2=size(comp_real,2);
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end
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switch obj.M
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case 2
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% 2-ASK
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data_out=comp_real(:,:,1);
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case 4
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% 4-ASK
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data_out=[comp_real(:,:,2); ones(s1,s2) - comp_real(:,:,1) + comp_real(:,:,3)];
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case 6
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data_in = data_in/(sqrt(mean(abs(data_in).^2)));
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data_in = data_in*sqrt(10);
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if size(data_in,2) > 1
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data_in = data_in.';
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end
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if length(data_in)/2 ~= round(length(data_in)/2)
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data_in = [data_in;0];
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end
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m = 1;
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for n = 1:2:length(data_in)
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dist = sqrt((data_in(n)-obj.thresholds(:,1)).^2+(data_in(n+1)-obj.thresholds(:,2)).^2);
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[~,dd_idx] = min(dist);
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% dec_out(n:n+1) = LUT(dd_idx,:);
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data_out(m:m+4) = bitget(dd_idx-1,5:-1:1);
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m = m+5;
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end
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case 8
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% 8-ASK
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data_out=[comp_real(:,:,4);
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comp_real(:,:,1)-comp_real(:,:,3)+comp_real(:,:,5)-comp_real(:,:,7);
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1-comp_real(:,:,2)+comp_real(:,:,6)];
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case 16
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% 16-ASK
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data_out=[comp_real(:,:,8);
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comp_real(:,:,1)-comp_real(:,:,3)+comp_real(:,:,5)-comp_real(:,:,7)+comp_real(:,:,9)-comp_real(:,:,11)+comp_real(:,:,13)-comp_real(:,:,15);
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comp_real(:,:,2)-comp_real(:,:,6)+comp_real(:,:,10)-comp_real(:,:,14);
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1-comp_real(:,:,4)+comp_real(:,:,12)];
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end
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data_out = data_out';
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end
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function [data_out] = decide_pamlevel(obj,data_in,options)
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arguments
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obj
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data_in Signal
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options.symbol_levels = []
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end
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%A) normally return the preproduct of the decision
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a = squeeze(repmat(real(data_in.signal),[1 1 length(obj.thresholds)])); %Eingangssignal in 3 spalten
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b = squeeze(repmat(reshape(obj.thresholds(:).',[1 1 length(obj.thresholds)]),[1 length(data_in.signal) 1])); %Threshold in 3 Spalten
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comp_real = a > b; %check for each symbol/ sampling if it exeeds the obj.thresholdseshold 1, 2 or 3
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data_out = data_in;
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data_out.signal = sum(comp_real,2);
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%Option: return the actual level values/ just map onto given
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%symbol levels
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if ~isempty(options.symbol_levels)
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data_out.signal = options.symbol_levels(data_out.signal+1);
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end
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end
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function [out] = separate_pamlevels(obj,data_in)
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%data_in is Signal class
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%A) normally return the preproduct of the decision
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a = squeeze(repmat(real(data_in.signal),[1 1 length(obj.thresholds)])); %Eingangssignal in 3 spalten
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b = squeeze(repmat(reshape(obj.thresholds(:).',[1 1 length(obj.thresholds)]),[1 length(data_in.signal) 1])); %Threshold in 3 Spalten
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comp_real = a > b; %check for each symbol/ sampling if it exeeds the obj.thresholdseshold 1, 2 or 3
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comp_real_sum = sum(comp_real,2);
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out = NaN(length(data_in),length(obj.thresholds)+1);
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for idx = 1:length(data_in)
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out(idx,comp_real_sum(idx)+1) = data_in.signal(idx);
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end
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end
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function [Signal_out] = quantize(obj,Signal_in)
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constellation = obj.get_levels();
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constellation = constellation ./ rms(constellation);
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Signal_out = Signal_in;
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dist = abs(Signal_in.signal - constellation);
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[~,symbol_idx] = min(dist,[],2); % decision for closest constellation point
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Signal_out.signal = constellation(symbol_idx);
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Signal_out.signal = reshape(Signal_out.signal,size(Signal_in.signal));
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end
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end
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end
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