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MATLAB代做|FPGA代做-ML-ZF算法的matlab仿真

时间:2021-12-13 2:57:02 点击:

  核心提示:MATLAB代做|FPGA代做-ML-ZF算法的matlab仿真...
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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作者:ML-ZF算法 来源:ML-ZF算法
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