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Silas Oettinghaus
2026-04-23 16:16:45 +02:00
parent 5446275a5b
commit 8fd68c0a54
6 changed files with 104 additions and 183 deletions
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100
Theory/Signal_Processing/partial_response_filter.m
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@@ -1,100 +0,0 @@
% DUOBINARY CLASSES
% Define the filter taps
h_ = {1,[1 1],[1 2 1],[1 3 3 1]};
desc = {'$1$','$(1+D)$','$(1+D)^2$','$(1+D)^3$'};
for i = 1:length(h_)
h = h_{i};
[H, w] = freqz(h, 1, 1024, 1);
figure(1);
hold on
plot(w, 10*log10(abs(H)), 'LineWidth', 1, 'DisplayName',desc{i}); %todo
xlabel('Normalized Frequency');
ylabel('Amplitude in dB');
grid on;
ylim([-20,10]);
end
legend
beautifyBERplot("logscale",false,"setmarkers",false);
% mat2tikz_improved("C:/Users/Silas/Documents/6971e0b65b380ca6d71c837f/03_DSP_Techniques/tikz/duobinary_response.tikz")
%%
% 1+ alpha D
% Define the filter taps
h_ = {[1, 0],[1 0.2],[1 0.4],[1 0.6],[1 0.8],[1 1]};
for i = 1:length(h_)
h = h_{i};
[H, w] = freqz(h, 1, 1024, 1);
figure(2);
hold on
plot(w, 10*log10(abs(H)), 'LineWidth', 2,'DisplayName',sprintf('$\\alpha = %.1f$', h(end))); %todo
xlabel('Normalized Frequency');
ylabel('Amplitude in dB');
grid on;
ylim([-15,5])
end
legend
beautifyBERplot("logscale",false,"setmarkers",false);
% mat2tikz_improved("C:/Users/Silas/Documents/6971e0b65b380ca6d71c837f/03_DSP_Techniques/tikz/partial_response.tikz")
%%
useprbs = 0;
M = 4;
randkey = 2;
fsym = 112e9;
%%%%% PRBS Generation in correct shape for Modulation Format %%%%%%
O = 19; %order of prbs
N = 2^(O-1); %length of prbs
[~,seed] = prbs(O,1); %initialize first seed of prbs
bitpattern=[];
if useprbs
for i = 1:log2(M)
[bitpattern(:,i),seed] = prbs(O,N,seed);
end
else
s = RandStream('twister','Seed',randkey);
for i = 1:log2(M)
bitpattern(:,i) = randi(s,[0 1], N, 1);
end
end
if M == 6
bitpattern = reshape(bitpattern,[],1);
bitpattern = bitpattern(1:end-mod(length(bitpattern),5));
end
Tx_bits = Informationsignal(bitpattern);
Digi_Mod = PAMmapper(M,0);
Symbols_tx = Digi_Mod.map(Tx_bits);
Symbols_tx.fs = fsym;
cnt = 1;
Symbols = Symbols_tx;
Symbols = Duobinary().precode(Symbols);
Symbols = Duobinary().encode(Symbols);
figure()
histogram()
% Symbols = Duobinary().decode(Symbols);
% coeff = [1,0.5];
%
% Symbols.signal = filter(coeff, 1, Symbols.signal);
% Symbols.spectrum("fignum",2,"displayname",['coeff:',num2str(coeff)],"normalizeTo0dB",1);