ML-ZF算法的matlab仿真

ゝ一世哀愁。 2022-09-11 10:28 186阅读 0赞

clear all;clc  
N = 2; % Number of Transmit antennas  
M = 2; % Number of Receive antennas  
EbNoVec = 5:3:11; % Eb/No in dB  
modOrd = 2; % constellation size = 2^modOrd  
numSym = N; % number of symbols

% Seed states for repeatability  
rand('twister', 12345); randn('state', 98765);

% Get all bit combinations for ML receiver  
bits = de2bi(0:2^(modOrd\*N)-1, 'left-msb')';  
% Split them per Transmit antenna  
b = zeros(N, modOrd, length(bits));  
for i = 1:length(bits)  
b(:, :, i) = reshape(bits(:,i), modOrd, N)';  
end

% Preallocate variables for speed  
dist = zeros(length(bits), 1);  
\[BER\_ZF, BER\_MMSE, BER\_ML\] = deal(zeros(1, length(EbNoVec)));

% Create QPSK mod-demod objects  
hMod = modem.pskmod('M', 2^modOrd, 'SymbolOrder', 'gray', 'InputType', 'bit');  
hDemod = modem.pskdemod(hMod);

% Set up a figure for visualizing BER results  
h = gcf; grid on; hold on;  
set(gca,'yscale','log','xlim',\[EbNoVec(1)-0.01, EbNoVec(end)\],'ylim',\[1e-4 1\]);  
xlabel('Eb/No (dB)'); ylabel('BER'); set(h,'NumberTitle','off');  
set(h, 'renderer', 'zbuffer'); set(h,'Name','Spatial Multiplexing');  
title('2x2 Uncoded QPSK System');

% Loop over selected EbNo points  
for idx = 1:length(EbNoVec)  
nErrs\_zf = 0; nErrs\_mmse = 0; nErrs\_ml = 0;  
nBits = 0;  
while ( ((nErrs\_zf < 100) || (nErrs\_mmse < 100) || (nErrs\_ml < 100)) ...  
&& (nBits < 1e4))  
% Create array of bits to modulate  
msg = randint(modOrd, numSym, 2);

% Modulate data  
source = modulate(hMod, msg);

% Split source among N transmitters (symbol-wise)  
Tx = reshape(source, N, numel(source)/N); clear source;

% Flat Rayleigh Fading - independent links  
RayleighMat = (randn(M, N) + sqrt(-1)\*randn(M, N))/sqrt(2);

% Calculate SNR from EbNo  
snr = EbNoVec(idx) + 10\*log10(modOrd);

% Add channel noise power to faded data  
r = awgn(RayleighMat\*Tx, snr); clear Tx;  
r\_store = r;

% Assume perfect channel estimation  
H = RayleighMat;

% Zero-Forcing SIC receiver  
E\_zf = zeros(modOrd, numSym); k = zeros(N, 1);  
% Initialization  
G = pinv(H);  
\[val, k0\] = min(sum(abs(G).^2,2));  
% Start Zero-Forcing Nulling Loop  
for n = 1:N  
% Find best transmitter signal using minimum norm  
k(n) = k0;

% Select Weight vector for best transmitter signal  
w = G(k(n),:);

% Calculate output for transmitter n and demodulate bitstream  
y = w \* r;  
E\_zf(:, k(n):N:end) = reshape(demodulate(hDemod, y), modOrd, numSym/N);

% Subtract effect of the transmitter n from received signal  
z = modulate(hMod, demodulate(hDemod, y));  
r = r - H(:, k(n))\*z;

% Adjust channel estimate matrix for next minimum norm search  
H(:, k(n)) = zeros(M, 1);  
G = pinv(H);  
for aa = 1:n  
G(k(aa), :) = inf;  
end  
\[val, k0\] = min(sum(abs(G).^2,2));  
end

% Restore variables for next receiver  
H = RayleighMat; r = r\_store;

% MMSE SIC receiver  
E\_mmse = zeros(modOrd, numSym); k = zeros(N, 1);  
% Initialization  
G = inv(H'\*H + N/(10^(0.1\*snr))\*eye(N)) \* H';  
\[val, k0\] = min(sum(abs(G).^2,2));  
% Start MMSE Nulling Loop  
for n = 1:N  
% Find best transmitter signal using Min Norm  
k(n) = k0;

% Select Weight vector for best transmitter signal  
w = G(k(n),:);

% Calculate output for transmitter n and demodulate bitstream  
y = w \* r;  
E\_mmse(:, k(n):N:end) = reshape(demodulate(hDemod, y), modOrd, numSym/N);

% Subtract effect of the transmitter n from received signal  
z = modulate(hMod, demodulate(hDemod, y));  
r = r - H(:, k(n))\*z;

% Adjust channel estimate matrix for next min Norm search  
H(:, k(n)) = zeros(M, 1);  
G = inv(H'\*H + N/(10^(0.1\*snr))\*eye(N)) \* H';  
for aa = 1:n  
G(k(aa), :) = inf;  
end  
\[val, k0\] = min(sum(abs(G).^2,2));  
end

% Restore variables for next receiver  
H = RayleighMat; r = r\_store;

% ML receiver  
for i = 1:2^(modOrd\*N)  
% Signal constellation for each bit combination  
sig = modulate(hMod, b(:, :, i)').';

% Distance metric for each constellation  
dist(i) = sum(abs(r - H\*sig).^2);  
end  
% Get the minimum  
\[notUsed, val\] = min(dist);  
E\_ml = b(:,:,val)'; % detected bits

% Collect errors  
nErrs\_zf = nErrs\_zf + biterr(msg, E\_zf);  
nErrs\_mmse = nErrs\_mmse + biterr(msg, E\_mmse);  
nErrs\_ml = nErrs\_ml + biterr(msg, E\_ml);

nBits = nBits + length(msg(:));  
end

% Calculate BER for current point  
BER\_ZF(idx) = nErrs\_zf./nBits;  
BER\_MMSE(idx) = nErrs\_mmse./nBits;  
BER\_ML(idx) = nErrs\_ml./nBits;

% Plot results  
semilogy(EbNoVec(1:idx), BER\_ZF(1:idx), 'r\*', ...  
EbNoVec(1:idx), BER\_MMSE(1:idx), 'bo', ...  
EbNoVec(1:idx), BER\_ML(1:idx), 'gs');  
legend('ZF-SIC', 'MMSE-SIC', 'ML');  
drawnow;  
end

% Draw the lines  
semilogy(EbNoVec, BER\_ZF, 'r-', EbNoVec, BER\_MMSE, 'b-', ...  
EbNoVec, BER\_ML, 'g-');  
hold off;

openfig('spatMuxResults.fig');

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