231 lines
7.2 KiB
Matlab
231 lines
7.2 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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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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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.logbookentry();
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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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signalclass_in = signalclass_in.logbookentry();
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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 log2(obj.M)
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case 1
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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 2
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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 .* 1/sqrt(5);
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case 3
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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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case 4
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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 log2(obj.M)
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case 1
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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 2
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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 3
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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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case 4
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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 [data_out] = demap_(obj,data_in)
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data_in= data_in';
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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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switch log2(obj.M)
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case 1
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% 2-ASK
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data_out=comp_real(:,:,1);
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case 2
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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 3
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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 4
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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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%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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end
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end
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