（23）DBPSK信号在Rayleigh衰落信道条件下的传输性能仿真

news2024/10/9 10:18:03

文章目录

前言
一、MATLAB仿真代码
二、仿真结果画图

前言

此示例创建了一个【频率平坦的瑞利衰落信道】对象，并使用该对象来对DBPSK信号进行衰落处理，衰落之后增加了不同信噪比的AWGN，计算出不同的信噪比值计算误码率，并和理论误码率进行了对比。

一、MATLAB仿真代码

代码如下：

% Create Rayleigh fading channel objects
chan = comm.RayleighChannel('SampleRate',1e4,'MaximumDopplerShift',100);


M = 2;                                   % DBPSK modulation order
tx = randi([0 M-1],50000,1);             % Generate a random bit stream

% Create modulator, and demodulator objects
mod = comm.DBPSKModulator;
demod = comm.DBPSKDemodulator;

dpskSig = mod(tx);                       % dpskSig=±1序列

% Apply the channel effects
sig_test = 1j*ones(50000,1);
fadedTestSig = chan(sig_test);           % 衰落
fadedSig = dpskSig.*fadedTestSig;        % 衰落作用与发射信号，得到衰落信号


figure()
plot(20*log10(abs(fadedSig)))
hold on
grid on
xlim([0,10000])
title('DBPSK衰落信号的功率')
xlabel('samples');
ylabel('Signal power(dB)');


% Create an AWGN channel object.
awgnChan = comm.AWGNChannel('NoiseMethod', 'Signal to noise ratio (SNR)');

% Create an error rate calculator object.
errorCalc = comm.ErrorRate;

% Compute error rate for different values of SNR.
SNR = 0:2:20;                % Range of SNR values, in dB.
numSNR = length(SNR);
berVec = zeros(3, numSNR);   % Preallocate a vector for BER results 

for n = 1:numSNR
   awgnChan.SNR = SNR(n);
   rxSig = awgnChan(fadedSig);   % Add Gaussian noise
   rx = demod(rxSig);            % Demodulate
   reset(errorCalc)
  
   berVec(:,n) = errorCalc(tx,rx); % Compute error rate.
end
BER = berVec(1,:);


% Compute theoretical performance results, for comparison.
BERtheory = berfading(SNR,'dpsk',M,1);

% Plot BER results.
figure()
semilogy(SNR,BERtheory,'b-',SNR,BER,'r*','LineWidth',1);
grid on;
xlabel('SNR (dB)');
ylabel('BER');
title('Binary DPSK over Rayleigh Fading Channel');
legend('Theoretical BER','Empirical BER');