BPSK仿真matlab代码

BPSK Simulation Code in MATLAB
Below is a comprehensive example of MATLAB code designed to simulate Binary Phase Shift Keying (BPSK) modulation and demodulation through an Additive White Gaussian Noise (AWGN) channel. The provided script includes generating binary data, modulating it using BPSK, passing the signal through an AWGN channel, demodulating received symbols, calculating Bit Error Rate (BER), and plotting BER versus (E_b/N_0).
Generating Binary Data Sequence
Firstly, generate random binary bits which will serve as input information sequence.
% Parameters setup
numBits = 1e5; % Number of transmitted bits
EbNoVec = 0:2:12; % Range of Eb/No values in dB
berEst = zeros(size(EbNoVec)); % Initialize estimated bit error rate vector

for k = 1:length(EbNoVec)
EbN0 = EbNoVec(k);

% Generate random binary data stream
txData = randi([0 1], numBits, 1);

% Modulate with BPSK scheme
bpskModulatedSignal = 2*txData - 1;
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The above section initializes parameters such as number of bits (numBits) and range of ( E_b / N_0 ) ratios (EbNoVec). Random binary sequences are generated by randi function where each element represents one bit either '0' or '1'. These bits get mapped into constellation points {-1,+1} via simple linear transformation[^2].
Passing Through AWGN Channel & Demodulation Process
Next part introduces noise according to specified Signal-to-Noise Ratio (( SNR )) level corresponding to current iteration value from loop variable EbN0.
snrLinear = 10^(EbN0/10)*log2(2)/mean(abs(bpskModulatedSignal).^2);

% Pass through AWGN channel model
rxSignal = awgn(bpskModulatedSignal,snrLinear,'measured');

% Perform coherent detection at receiver side
detectedSymbols = sign(real(rxSignal));

% Convert back to original binary format
rxBits = (detectedSymbols + 1)/2;

% Calculate errors between transmit and receive ends
errCount = sum(txData ~= rxBits);

berEst(k) = errCount / length(txData);
end
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Here, awgn() adds white gaussian noise based on given ( SNR ). Coherent detection involves determining whether incoming symbol lies closer to positive or negative axis point (-1/+1). Finally, hard decision mapping converts these decisions back into binary form before comparing against initial transmission for computing total count of discrepancies leading towards estimation of average probability per bit being erroneous during entire process run-time[^4].
Plotting Results
Lastly, visualize obtained results showing relationship among different levels of ( E_b/N_0 ) ratio vs computed empirical probabilities derived earlier within main processing block.
semilogy(EbNoVec, berEst, '-o')
grid on
xlabel('E_b/N_0 (dB)')
ylabel('Bit Error Probability')
title('Performance Analysis of Uncoded BPSK Over AWGN Channel')
legend({'Simulation'}, 'Location', 'Best');
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This plot uses logarithmic scale along y-axis due to wide dynamic span typically observed across various communication systems when measuring small quantities like error rates especially under good conditions having very low failure occurrences expected theoretically predicted curves could also overlay here but omitted intentionally keeping focus solely upon practical outcomes achieved experimentally throughout execution cycle described previously.