瑞利衰落信道模型的研究与仿真.docx

瑞利衰落信道模型的研究与仿真 瑞利衰落信道模型的研究与仿真 matlab 程序 % 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