瑞利信道怎么用 matlab做仿真

如果是最简单的平坦瑞利衰落，将星座图映射后的信号，乘以功率为1的复高斯信号就完成了搜消。在接收端判决前，族漏闭除以信道系数（即前边的复高斯信号），就可以判决了。这样肯定是0误码率。在加入衰落后，还可以加入不同功率的白噪声，进而得到SNR－BER曲线.

如果多径，还要在接收端引入均衡，均衡有很兆裂多种。

可以看看simulink中自带的例子

饿了。 。 。

您的H1确实是瑞利分布，但幅度通道H1啊，我们说“瑞利路”，不意味没御着真正的通道，而是一个复杂的通道，通道幅度瑞利分布，所以你枯物岩H1 = ABS（ H1）“这句话应该被删除。

其次，就是我们常说的信道响应，不包含??高斯白噪声，也就蚂铅是说，只H此人。

直接IFFT（H1 ）是信道响应。************************************

建模许多通信模块失败的反映，说明这里也不是很清楚。建议向老师

% written by Amir Sarrafzadeh (14Jan2008)

% this function generates normalized rayleigh samples based on Inverse DFT

% method as was proposed by David J. Young, and Norman C. Beaulieu

% "The Generation of Correlated Rayleigh Random Variates by Inverse

% Discrete Fourier Transform, "

% Sample Use:

% chan=genRayleighFading(512,ceil(10000/512),1e4,100)

% chan=chan(1:10000)

% where 10000=number of needed samples

% parameters:

% fftsize: size of fft which used

% numBlocks: number of samples/fftsize

% fs: sampling frequency(Hz)

% fd: doppler shift(Hz)

function [ outSignal ] = genRayleighFading( fftSize,numBlocks,fs,fd )

numSamples=fftSize*numBlocks%total number of samples

fM=fd/fs %normalized doppler shift

NfM=fftSize*fM

kM=floor(NfM)%maximum freq of doppler filter in FFT samples

doppFilter=[0,1./sqrt(2*sqrt(1-(((1:kM-1)./NfM).^2))),sqrt((kM/2)*((pi/2)-atan((kM-1)/sqrt(2*kM-1)))),...

zeros(1,fftSize-2*kM-1),sqrt((kM/2)*((pi/2)-atan((kM-1)/sqrt(2*kM-1)))),1./sqrt(2*sqrt(1-(((kM-1:-1:1)./NfM).^2)))].'

sigmaG=sqrt((2*2/(fftSize.^2))*sum(doppFilter.^2))

gSamplesI=randn(numSamples,2) %i.i.d gaussian input samples (in phase)

gSamplesQ=randn(numSamples,2) %i.i.d gaussian input samples (quadrature phase)

gSamplesI=(1/sigmaG)*(gSamplesI(:,1)+1j*gSamplesI(:,2))

gSamplesQ=(1/sigmaG)*(gSamplesQ(:,1)+1j*gSamplesQ(:,2))

%filtering

filterSamples=kron(ones(numBlocks,1),doppFilter)

gSamplesI=gSamplesI.*filterSamples

gSamplesQ=gSamplesQ.*filterSamples

freqSignal=gSamplesI-1j*gSamplesQ

freqSignal=reshape(freqSignal,fftSize,numBlocks)

outSignal=ifft(freqSignal,fftSize)

outSignal=abs(outSignal(:))%Rayleigh distributed signal

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