Estimate Turbo Code BER Performance in AWGN

Open Live Script

Simulate an end-to-end communication link employing 16-QAM using turbo codes in an AWGN channel. Estimate the bit error rate.

Initialize Simulation

Set the modulation order and the range of Eb/No values to evaluate. Set the packet length to 500.

rng(10,'twister');
M = 16; % Modulation order
bps = log2(M); % Bits per symbol
EbNo = (2:0.5:4);
pktLen = 500;

Initialize the bit error rate vector.

ber = zeros(size(EbNo));

Create System objects for a turbo encoder and decoder pair, where the interleaver indices are supplied as input arguments.

turboEnc = comm.TurboEncoder('InterleaverIndicesSource','Input port');

turboDec = comm.TurboDecoder('InterleaverIndicesSource','Input port', ...
    'NumIterations',4);

Create an AWGN channel System object and an error rate counter System object.

awgnChannel = comm.AWGNChannel('NoiseMethod','Variance','Variance',1);
errorRate = comm.ErrorRate;

Use the packet length and turbo encoder settings to determine actual transmitted bit rate. The turbo-coding objects are initialized to use rate-1/2 trellis for their constituent convolutional codes, resulting in a turbo encoder output with 2 parity bit streams, (in addition to the systematic stream) and 12 tail bits for the input packet. The 12 tail bits are due to the specified constraint length of 4 per constituent encoder, which leads to 3-bit outputs per stream, for a total of 4 streams (S1 P1 S2 P2).

    rate = pktLen/(3*pktLen+4*3);

Main Processing Loop

The processing loop performs the following steps:

  • Generate random binary data

  • Generate random interleaver indices

  • Turbo encode the data

  • Apply 16-QAM modulation

  • Pass the modulated signal through an AWGN channel

  • Demodulate the noisy signal using an LLR algorithm

  • Turbo decode the data

  • Calculate the error statistics

 for k = 1:length(EbNo)

Initialize the error statistics vector, signal-to-noise ratio, and noise variance. Update the AWGN channel System object noise variance value.

    errorStats = zeros(1,3);
    EsNo = EbNo(k) + 10*log10(bps);       
    snrdB = EsNo + 10*log10(rate); % in dB
    noiseVar = 1./(10.^(snrdB/10));  
    awgnChannel.Variance = noiseVar;

    while errorStats(2) < 100 && errorStats(3) < 1e7
        % Generate random binary data
        data = randi([0 1],pktLen,1);
        % Interleaver indices
        intrlvrInd = randperm(pktLen);
        % Turbo encode the data
        encodedData = turboEnc(data,intrlvrInd);
        % Modulate the encoded data
        modSignal = qammod(encodedData,M,'InputType','bit','UnitAveragePower',true);
        % Pass the signal through the AWGN channel
        rxSignal = awgnChannel(modSignal);
        % Demodulate the received signal
        demodSignal = qamdemod(rxSignal,M,'UnitAveragePower',true,'OutputType','llr','NoiseVariance',noiseVar);
        % Turbo decode the demodulated signal. Because the bit mapping from the
        % demodulator is opposite that expected by the turbo decoder, the
        % decoder input must use the inverse of demodulated signal.
        rxBits = turboDec(-demodSignal,intrlvrInd);
        % Calculate the error statistics
        errorStats = errorRate(data,rxBits);
    end
    % Save the BER data and reset the bit error rate object
     ber(k) = errorStats(1);
     reset(errorRate)
end

Plot the bit error rate and compare it to the uncoded bit error rate.

semilogy(EbNo,ber,'-o')
grid
xlabel('Eb/No (dB)')
ylabel('Bit Error Rate')
uncodedBER = berawgn(EbNo,'qam',M); % Estimate of uncoded BER
hold on
semilogy(EbNo,uncodedBER)
legend('Turbo','Uncoded','location','sw')

See Also

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