matlab信道仿真经典源程序

% %

% Rayleigh Fading Channel Signal Generator

% Using the Dent Model (a modification to the Jakes Model) %

% Last Modified 10/18/05 %

% Author: Avetis Ioannisyan (avetis@60ateight.com) % %

% Usage:

% [omega_mTau, Tk] =

% ai_RayCh(NumAngles, Length, SymbolRate, NumWaveforms, CarrierFreq, Velocity) %

% Where the output omega_mTau is a time scaling factor for plotting

% normalized correlations. The LAGS value output by [C,LAGS] = XCORR(...) % should be multiplied by the omega_mTau scaling factor to properly display % axis. Tk is a two dimensional vector [M, N] = SIZE(Tk) with

% M=numWaverorms and N=Length specified in the RayCh(...) function call %

% And the input variables are: %

% NumAngles - scalar power of 2, NumAngles > 2^7 is used to specify the % number of equally strong rays arriving at the receiver. It used to

% compute the number of oscillators in the Dent model with N0 = numAngles/4 %

% Length - scalar preferably power of 2 for faster computation, Length > 2^17 % is used to specify the length of the generated sequence. Lengths near 1E6 % are close to realistic signals %

% SymbolRate - scalar power of 2 and is in kilo-symbols-per-sec is used to % specify what should be the transmission data rate. Slower rates will % provide slowly fading channels. Normal voice and soem data rates are % 64-256 ksps %

% NumWaveforms - scalar used to specify how many 'k' waveforms to generate % in the model. NumWaveforms > 2 to properly display plots %

% CarrierFreq - scalar expressed in MHz is the carrier frequency of the % tranmitter. Normally 800 or 1900 MHz for mobile comms %

% Velocity - scalar expressed in km/hr is the speed of the receiver. % 100 km/hr = 65 mi/hr. Normal values are 20-130 km/hr %

% Usage Examples:

% [omega_mTau, Tk] = ai_RayCh(2^7, 2^18, 64, 2, 900, 100) %

% where %

% NumAngles=2^7, Length=2^18, symbolRate=64, NumWaveforms=2, carrierFreq=900, Velocity=100

% [omega_mTau, Tk] = RayCh(NumAngles, Length, symbolRate, NumWaveforms, % carrierFreq, Velocity); % %

function [omega_mTau, Tk] = ai_RayCh(NumAngles, Length, symbolRate, NumWaveforms, carrierFreq, Velocity)

% Number of oscillators N0 = NumAngles/4;

% Maximum Doppler shift of carrier at some wavelength omega_m = (2*pi) * fm(Velocity, carrierFreq); % specify variance of the Rayleigh channel

% use this for *constant* variange - requires changing other params in prog sigma2 = 10;

% make sigma2 a gaussian RV around u = sigma2 and var = sigma2/5

% use for *non constant* variaance - requires changing other params in prog sigma2 = sigma2 + sqrt(sigma2/5) .* randn(1,NumWaveforms); % Initialize phases

alpha_n = []; beta_n = []; theta_nk = []; % make a hadamard matrix Ak = hadamard(N0);

% determine phase values 'alpha' and 'beta' n=[1:N0];

alpha_n = 2*pi*n/NumAngles - pi/NumAngles; beta_n = pi*n/N0;

% convert to time scale using 'fs' sampling frequency

t=[1/(symbolRate*1000):1/(symbolRate*1000):1/(symbolRate*1000) * Length];

Tk = [];

for q = 1 : NumWaveforms

rand('state',sum(100*clock)) % reset randomizer

theta_nk = rand(1,length(n)) * 2 *pi; % create uniform random phase in range [0,2pi]

sumRes = 0; for i = 1 : N0

term1 = Ak(NumWaveforms,i);

term2 = cos(beta_n(i)) + j*sin(beta_n(i));

term3 = cos(omega_m .* t .* cos(alpha_n(i)) + theta_nk(i)); sumRes = sumRes + (term1 .* term2 .* term3); end

Tk(q,:) = sqrt(2/N0) .* sumRes;

% use line below to apply *non-constant* variance

Tk(q,:) = repmat(10.^(sigma2(q)/20),1, Length) .* Tk(q,:); %apply variable in dB end

% apply *constant* variance unilaterly in dB % Tk = repmat(10^(sigma2/20), k, Length) .* Tk;

% plot results

figure(20); subplot(3,1,1); semilogy(t,abs(Tk(1,:))); xlabel('Time (sec)'); ylabel('Signal Strength (dB)');

title(['Received Envelope, Symbol Rate = ', num2str(symbolRate), ',Carrier = ', num2str(carrierFreq), ', Velocity = ', num2str(Velocity)]); % compute auto and cross correlations and plot them

omega_mTau = (1/(symbolRate*1000)) * (omega_m/(2*pi)); % compute omega_m * tau scaling

[C1, Lags] = crosscorr(Tk(1,:), Tk(2,:), 20000); [C2, Lags2] = autocorr(Tk(1,:), 20000);

figure(20); subplot(3,1,3); plot(Lags * omega_mTau, C1);

xlabel('Normalized Time Delay'); ylabel('Normalized Crosscorrelation'); title('Crosscorrelation between waveforms k=1 and k=2'); figure(20); subplot(3,1,2); plot(Lags2 * omega_mTau, C2);

xlabel('Normalized Time Delay'); ylabel('Normalized Autocorrelation'); title('Autocorrelation of the first waveform k=1');