I see you’re trying to simulate a communication system using MATLAB focusing mainly BER for a convolutionally encoded and Viterbi-decoded signal but there are some corrections in your code. Follow these steps:
1.) Change the constraint length in poly2trellis from 4 to 3 to match the generator polynomials [7 5 3]. This will resolve the error related to the constraint length mismatch.
% Trellis definition
trellis_poly = poly2trellis(3, [7 5 3]); % Constraint length 3, using octal representation
2.) Adjust the noise variance calculation to ensure that it aligns with the expected SNR and code rate.
% Noise addition
noise_variance = 1/(2 * code_rate * 10^(SNR(i)/10));
noise = sqrt(noise_variance) * randn(size(codeword_bpsk));
corrupted_codeword = codeword_bpsk + noise;
3.) Introduce a scaling and quantization process for the corrupted_codeword to prepare it for soft-decision decoding. This involves normalilizing the corrupted_codeword to fit within the range expected by the vitdec function and quantizing the normalized values into integers ranging from 0 to 7 (for 3-bit soft decision decoding).
L = 3; % Number of soft decision bits
max_val = 2^L - 1;
% Normalize and scale
scaled_codeword = (corrupted_codeword + 1) * (max_val / 2);
quantized_codeword = round(scaled_codeword);
quantized_codeword = min(max(quantized_codeword, 0), max_val);
4.) Use the “vitdec” function with the 'soft' option and specify the number of soft decision bits as 3.
% Decoding
traceback_length = 5 * v;
decoded = vitdec(quantized_codeword, trellis_poly, traceback_length, 'term', 'soft', L);
5.) Calculate the Bit Error Rate (BER) by comparing the original data with the decoded data, ensuring the trailing bits are excluded from the comparison.
% BER Calculation
BER(i) = biterr(data(i, 1:end-terminate_length), decoded(1:input_size)) / input_size;
I ran this code and got this figure:
You can refer to the following documentation for more help:
