receiver_rls.m

OFDM信道估计和均衡的仿真程序

%+----------------------------------------------------------------+
%|                                                                |
%|  Name: receiver_rls.m                                          |
%|  Author: James Shen                                            |
%|  Description: Takes ina signal and tries to demodulate it.     |
%|  We assume that we are receiving pilot signals so that the     |
%|  desired signal is know already. What happens in a real        |
%|  implementation is that the weiner coefficients derived will   |
%|  continue to be used with data and the desired signal will     |
%|  then be the decovered signal. This will accumulate errors in  |
%|  the coefficients until the next batch of pilot symbols        |
%|  arrive. Receives one OFDM.                                    |
%|                                                                |
%+----------------------------------------------------------------+
function[recovered_signal,w,e] = ... 
    receiver_rls(desired_signal, noisy_signal, FFTLen, CPLen, M, w, F);
% desired_signal The signal you want to obtain after filtering.
% noisy_signal   This signal is somehow correlated to the desired_signal.
% FFTLen         This is the size of the FFT to be used. Should be a power of
%                2. Hyoerlan works to 64 points.            
% M              Bits per symbol used in baseband mod and demod.
% w              The filter coefficients
% F              Samples taken for correlation and that of the Kalman filter.
% e              Error (desired_signal - noisy_signal)
% recovered _signal = the signal that is finally output.
%+----------------------------------------------------------------+
%|                    OFDM   RECEVIER                             |
%+----------------------------------------------------------------+
%======================= RLS Equalizer ================================
%======================= Initializations ================================
N = length(desired_signal);
P = zeros(F,1); % Inverse correlation matrix.
P = eye(F)*100;

udata = noisy_signal; % Noisy signal yet correlated to desired.
d = desired_signal;      % Desired signal.

lambda = 0.9;   % Weighting factor 0.95<=w<=1
%====================== Filter Iterations ==============================
for n=F:N
   u = flipud(udata(n:-1:(n-F+1)).'); %col vector now
   K = P*u ./ ((1-lambda) + u.'*P*u);
   y(n) = w.'*u;
   e(n) = d(n) - y(n);
   w = w + K*e(n);
   P = (1/(1-lambda))*(eye(F) - K*u.')*P;
end

output_ext = y; % ext means still with cyclic prefix extension.

%============== Gaurd Interval Removal (GIR) ==================
output_time_serial = output_ext((CPLen+1):FFTLen+CPLen);

%======== Serial To Parallel =================================
output_time_para = reshape(output_time_serial,FFTLen,1);

%================= FFT =================================
output_symbols = fft(output_time_para);

%================= Baseband Demodulation ===========================
output_para = qamdemod(output_symbols,FFTLen, M);

%================ Parallel to Serial ====================
output = reshape(output_para,1,FFTLen*M); % Time domain signal

%================ Returning output ===============================
recovered_signal = output;
w = w;
e = e;

% The code below should be commented out when
% running for more than one iteration as it just
% shows graphs for one symbol
%================ Error Calculation ===========================
error1= desired_signal - output_ext;
figure(1);
semilogy(abs(error1));

%================ Graphs ===========================
figure(2);
semilogy(abs(desired_signal), 'b'); hold on;
semilogy(abs(noisy_signal), 'g');
semilogy(abs(output_ext), 'r'); hold off;
figure(3);
semilogy(abs(e));