PR and DB fummelei
This commit is contained in:
@@ -3,22 +3,12 @@
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M = 4;
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apply_precode = 1;
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bitpattern = [];
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s = RandStream('twister','Seed',1);
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for i = 1:log2(M)
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N = 2^(17-1); %length of prbs
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bitpattern(:,i) = randi(s,[0 1], N, 1);
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end
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if M == 6
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bitpattern = reshape(bitpattern',[],1);
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bitpattern = bitpattern(1:end-mod(length(bitpattern),5));
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end
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bits = Informationsignal(bitpattern);
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bits = Signalgenerator( ...
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"form", signalform.prms, ...
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"M", M, ...
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"order", 15).process();
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symbols = PAMmapper(M,0).map(bits);
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bits_rx = PAMmapper(M,0).demap(symbols);
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[~,~,ber_direct,~] = calc_ber(bits.signal,bits_rx.signal,"skip_front",0,"skip_end",0,"returnErrorLocation",1);
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@@ -27,10 +17,6 @@ if apply_precode
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else
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symbols_tx = symbols;
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end
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disp(['Tx Sequenz: -- RMS:',sprintf('%.1f',rms(symbols_rx.signal)),' - - Levels -',num2str(numel(unique(symbols_rx.signal)))]);
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unique(symbols_tx.signal)
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disp('- - - - - - - - - -');
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show2Dconstellation(symbols_tx,symbols_tx,"displayname",'VNLE Out','fignum',2241);
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@@ -62,7 +48,7 @@ disp(['BER: ',sprintf('%.1E',ber),' - - PAM-',num2str(M)]);
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figure()
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subplot(1,2,1)
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histogram(symbols_tx.signal,100,'Normalization','count')
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histogram(symbols_tx.signal,100,'Normalization','probability')
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subplot(1,2,2)
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histogram(symbols_db.signal,100,'Normalization','count')
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histogram(symbols_db.signal,100,'Normalization','probability')
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@@ -4,46 +4,10 @@ randkey = 1;
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datarate = 448e9;
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fsym = round(datarate / log2(M)) ;
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%%%%% PRBS Generation in correct shape for Modulation Format %%%%%%
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O = 16; %O of prbs
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N = 2^(O); %length of prbs
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[~,seed] = prbs(O,1); %initialize first seed of prbs
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bitpattern=[];
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state = struct();
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para = struct();
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if M == 6
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para.bl = 2^(O-2);
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para.dimension = 5;
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else
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para.bl = 2^(O-1);
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para.dimension = log2(M); %2.5bits/sym -> 2 bit/sym
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end
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para.rand = 0;
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para.order = floor(O / log2(M));
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para.skip =0;
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para.bruijn = 0;
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para.reset_prms = 0;
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para.method = 1;
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data_in = [];
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global loop;
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loop = 0;
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[data_out,state_] = prms(data_in, state, para);
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loop = 1;
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[data_out,state_out] = prms(data_in, state_, para);
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bitpattern = data_out';
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if M == 6
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bitpattern = reshape(bitpattern',[],1);
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bitpattern = bitpattern(1:end-mod(length(bitpattern),5));
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end
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Tx_bits = Informationsignal(bitpattern);
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Tx_bits = Signalgenerator( ...
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"form", signalform.prms, ...
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"M", M, ...
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"order", 15).process();
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%%%%% Duobinary %%%%%%
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close all
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@@ -96,8 +60,7 @@ for n = 10
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Rx_bits = PAMmapper(M,0).demap(Symbols_rx);
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%%%%% Check BER of Bit Sequence %%%%%%
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%%%%% Check BER of Bit Sequence %%%%%
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[~,error_num(n+1),ber,error_pos] = calc_ber(Tx_bits.signal,Rx_bits.signal,"skip_front",10,"skip_end",10,"returnErrorLocation",1);
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% disp(['BER: ',sprintf('%.1E',ber),sprintf(' - Num. Err: %.1d',error_num(n+1)-2),' - - PAM-',num2str(M)]);
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@@ -70,10 +70,4 @@ h = heatmap(levels, levels, P, 'Colormap', parula, 'ColorbarVisible','on');
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colormap(gca,[[1,1,1];flip(cbrewer2('Spectral',100))]);clim([0,ceil(max(P(:))*10)/10]);
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h.XLabel = 'From state (level)';
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h.YLabel = 'To state (level)';
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h.Title = 'P(to | from)';
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%% 2) WEIGHTED TRANSITION GRAPH
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% Use dtmc if you have Econometrics Toolbox:
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mc = dtmc(P, 'StateNames', string(levels));
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figure('Name','Markov Graph (dtmc)');
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gp = graphplot(mc, 'ColorEdges',true, 'LabelEdges',true);
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h.Title = 'P(to | from)';
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@@ -0,0 +1,86 @@
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% DUOBINARY CLASSES
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% Define the filter taps
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h_ = {1,[1 1],[1 2 1],[1 3 3 1]};
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desc = {'$1$','$(1+D)$','$(1+D)^2$','$(1+D)^3$'};
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for i = 1:length(h_)
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h = h_{i};
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[H, w] = freqz(h, 1, 1024, 1);
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figure(1);
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hold on
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plot(w, 10*log10(abs(H)), 'LineWidth', 1, 'DisplayName',desc{i}); %todo
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xlabel('Normalized Frequency');
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ylabel('Amplitude in dB');
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grid on;
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ylim([-20,10]);
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end
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legend
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beautifyBERplot("logscale",false,"setmarkers",false);
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% mat2tikz_improved("C:/Users/Silas/Documents/6971e0b65b380ca6d71c837f/03_DSP_Techniques/tikz/duobinary_response.tikz")
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%%
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% 1+ alpha D
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% Define the filter taps
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h_ = {[1, 0],[1 0.3],[1 0.6],[1 0.9],[1 1],[1 2 1],[1 3 3 1]};
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col = {[0 0 0],[0.2000,0.6275,0.1725],[0.1216, 0.4706, 0.7059],[0.8902,0.1020,0.1098],...
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[0.2000,0.6275,0.1725],[0.9333, 0.9451, 0.9490],[0.9333, 0.9451, 0.9490]};
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sty = {':','-','-','-','--','--','--'};
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for i = 1:length(h_)
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h = h_{i};
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[H, w] = freqz(h, 1, 1024, 1);
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figure(2);
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hold on
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dn = sprintf('$(1+ %.1f \\, D)$', h(end));
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if i == 5
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dn = sprintf('$(1+D)$', h(end));
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elseif i == 6
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dn = sprintf('$(1+D)^2$');
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elseif i == 7
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dn = sprintf('$(1+D)^3$');
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elseif i == 1
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dn = sprintf('$1$');
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end
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plot(w, 10*log10(abs(H)), 'LineWidth', 2,'DisplayName',dn,'Color',col{i},'LineStyle',sty{i}); %todo
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xlabel('Normalized Frequency');
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ylabel('Amplitude in dB');
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grid on;
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ylim([-20,10])
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end
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legend
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beautifyBERplot("logscale",false,"setmarkers",false,"setcolors",false);
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% mat2tikz_improved("C:/Users/Silas/Documents/6971e0b65b380ca6d71c837f/03_DSP_Techniques/tikz/partial_response_2.tikz")
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%%
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useprbs = 0;
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M = 4;
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randkey = 2;
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fsym = 112e9;
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Tx_bits = Signalgenerator("form", signalform.prms,"M", M,"order", 17).process();
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Symbols_tx = PAMmapper(M,0).map(Tx_bits);
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Symbols_tx.fs = fsym;
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cnt = 1;
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Symbols = Symbols_tx;
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Symbols = Duobinary().precode(Symbols);
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Symbols = Duobinary().encode(Symbols);
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Symbols = Duobinary().decode(Symbols);
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for alpha = 0:0.2:1
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Symbols.signal = filter([1,alpha], 1, Symbols.signal);
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Symbols.spectrum("fignum",2,"displayname",['coeff:',num2str(alpha)],"normalizeTo0dB",1,"normalizeToNyquist",0,"normalizeToSamplingRate",1);
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end
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@@ -2,19 +2,10 @@
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M = 4;
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apply_precode_at_tx = 1;
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bitpattern = [];
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s = RandStream('twister','Seed',1);
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for i = 1:log2(M)
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N = 2^(17-1); %length of prbs
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bitpattern(:,i) = randi(s,[0 1], N, 1);
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end
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if M == 6
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bitpattern = reshape(bitpattern',[],1);
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bitpattern = bitpattern(1:end-mod(length(bitpattern),5));
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end
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bits = Informationsignal(bitpattern);
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bits = Signalgenerator( ...
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"form", signalform.prms, ...
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"M", M, ...
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"order", 15).process();
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symbols = PAMmapper(M,0).map(bits);
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@@ -23,12 +14,13 @@ if apply_precode_at_tx
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else
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symbols_tx = symbols;
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end
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disp(['Tx Sequenz: -- RMS:',sprintf('%.1f',rms(symbols_tx.signal)),' - - Levels -',num2str(numel(unique(symbols_tx.signal)))]);
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unique(symbols_tx.signal)
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disp('- - - - - - - - - -');
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symbols_tx.signal = awgn(symbols_tx.signal,20,"measured",1);
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% show2Dconstellation(symbols_tx,symbols_tx,"displayname",'VNLE Out','fignum',2241);
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show2Dconstellation(symbols_tx,symbols_tx,"displayname",'VNLE Out','fignum',2241);
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if apply_precode_at_tx
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@@ -1,100 +0,0 @@
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% DUOBINARY CLASSES
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% Define the filter taps
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h_ = {1,[1 1],[1 2 1],[1 3 3 1]};
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desc = {'$1$','$(1+D)$','$(1+D)^2$','$(1+D)^3$'};
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for i = 1:length(h_)
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h = h_{i};
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[H, w] = freqz(h, 1, 1024, 1);
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figure(1);
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hold on
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plot(w, 10*log10(abs(H)), 'LineWidth', 1, 'DisplayName',desc{i}); %todo
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xlabel('Normalized Frequency');
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ylabel('Amplitude in dB');
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grid on;
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ylim([-20,10]);
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end
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legend
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beautifyBERplot("logscale",false,"setmarkers",false);
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% mat2tikz_improved("C:/Users/Silas/Documents/6971e0b65b380ca6d71c837f/03_DSP_Techniques/tikz/duobinary_response.tikz")
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%%
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% 1+ alpha D
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% Define the filter taps
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h_ = {[1, 0],[1 0.2],[1 0.4],[1 0.6],[1 0.8],[1 1]};
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for i = 1:length(h_)
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h = h_{i};
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[H, w] = freqz(h, 1, 1024, 1);
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figure(2);
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hold on
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plot(w, 10*log10(abs(H)), 'LineWidth', 2,'DisplayName',sprintf('$\\alpha = %.1f$', h(end))); %todo
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xlabel('Normalized Frequency');
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ylabel('Amplitude in dB');
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grid on;
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ylim([-15,5])
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end
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legend
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beautifyBERplot("logscale",false,"setmarkers",false);
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% mat2tikz_improved("C:/Users/Silas/Documents/6971e0b65b380ca6d71c837f/03_DSP_Techniques/tikz/partial_response.tikz")
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%%
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useprbs = 0;
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M = 4;
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randkey = 2;
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fsym = 112e9;
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%%%%% PRBS Generation in correct shape for Modulation Format %%%%%%
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O = 19; %order of prbs
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N = 2^(O-1); %length of prbs
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[~,seed] = prbs(O,1); %initialize first seed of prbs
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bitpattern=[];
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if useprbs
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for i = 1:log2(M)
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[bitpattern(:,i),seed] = prbs(O,N,seed);
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end
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else
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s = RandStream('twister','Seed',randkey);
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for i = 1:log2(M)
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bitpattern(:,i) = randi(s,[0 1], N, 1);
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end
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end
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if M == 6
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bitpattern = reshape(bitpattern,[],1);
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bitpattern = bitpattern(1:end-mod(length(bitpattern),5));
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end
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Tx_bits = Informationsignal(bitpattern);
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Digi_Mod = PAMmapper(M,0);
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Symbols_tx = Digi_Mod.map(Tx_bits);
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Symbols_tx.fs = fsym;
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cnt = 1;
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Symbols = Symbols_tx;
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Symbols = Duobinary().precode(Symbols);
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Symbols = Duobinary().encode(Symbols);
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figure()
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histogram()
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% Symbols = Duobinary().decode(Symbols);
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% coeff = [1,0.5];
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%
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% Symbols.signal = filter(coeff, 1, Symbols.signal);
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% Symbols.spectrum("fignum",2,"displayname",['coeff:',num2str(coeff)],"normalizeTo0dB",1);
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