www.pudn.com > RayCh.rar > ai_RayCh.m

%  
% 
% 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');