MATlab-Program.rar_turbo_turbo code

% Define parameters. M = 16; % Size of signal constellation k = log2(M); % Number of bits per symbol n = 5e5; % Number of bits to process nsamp = 4; % Oversampling rate % Create a binary data stream as a column vector. x = randint(n,1); % Random binary data stream % Plot first 40 bits in a stem plot. stem(x(1:40),'filled'); title('Random Bits'); xlabel('Bit Index'); ylabel('Binary Value'); % Define a convolutional coding trellis and use it % to encode the binary data. t = poly2trellis([5 4],[23 35 0; 0 5 13]); % Trellis code = convenc(x,t); % Encode. coderate = 2/3; mapping = [0 1 3 2 4 5 7 6 12 13 15 14 8 9 11 10].'; % B. Do ordinary binary-to-decimal mapping. xsym = bi2de(reshape(code,k,length(code)/k).','left-msb'); % C. Map from binary coding to Gray coding. xsym = mapping(xsym+1); % Plot first 10 symbols in a stem plot. figure; % Create new figure window. stem(xsym(1:10)); title('Random Symbols'); xlabel('Symbol Index'); ylabel('Integer Value'); % Modulate using 16-QAM. y = modulate(modem.qammod(M),xsym); % Define filter-related parameters. filtorder = 40; % Filter order delay = filtorder/(nsamp*2); % Group delay (# of input samples) rolloff = 0.25; % Rolloff factor of filter % Create a square root raised cosine filter. rrcfilter = rcosine(1,nsamp,'fir/sqrt',rolloff,delay); % Plot impulse response. figure; impz(rrcfilter,1)