NOMA和OMA的matlab代码

NOMA和OMA是两种不同的多用户通信技术。以下是它们的Matlab代码示例。

NOMA代码示例：

clc;
clear;
close all;
m = 2; % number of users
n = 2^m; % number of subcarriers
N = 2; % number of messages
SNRdB = 0:2:20; % range of SNR in dB
SNR = 10.^(SNRdB/10); % SNR in linear scale
P = 1; % transmit power for all users
BER = zeros(1,length(SNR)); % initialize BER array
for i = 1:length(SNR)
    % generate random messages
    msg = randi([0 1], N, m);
    % generate random spreading sequences
    H = sqrt(0.5)*(randn(m,n)+1i*randn(m,n));
    % normalize spreading sequences
    H = H./sqrt(sum(abs(H).^2,2));
    % generate superimposed codewords for NOMA
    x = zeros(1,n);
    for j = 1:N
        x = x + sqrt(P)*msg(j,:)*H;
    end
    % generate noise
    sigma = sqrt(P./(SNR(i)*2*n));
    noise = sigma*(randn(1,n)+1i*randn(1,n));
    % generate received signal
    y = x + noise;
    % detection for NOMA
    msg_hat = zeros(N,m);
    for j = N:-1:1
        msg_hat(j,:) = (y*H'*H)^(-1)*y*H'*sqrt(P)*msg(j,:)';
        y = y - sqrt(P)*msg_hat(j,:)*H;
    end
    % calculate BER for NOMA
    BER(i) = sum(sum(msg~=msg_hat))/(N*m);
end
% plot BER vs SNR for NOMA
semilogy(SNRdB,BER,'-o','linewidth',2);
grid on;
xlabel('SNR (dB)');
ylabel('BER');
title('BER vs SNR for NOMA');

OMA代码示例：

clc;
clear;
close all;
m = 2; % number of users
n = 2^m; % number of subcarriers
N = 2; % number of messages
SNRdB = 0:2:20; % range of SNR in dB
SNR = 10.^(SNRdB/10); % SNR in linear scale
P = 1; % transmit power for all users
BER = zeros(1,length(SNR)); % initialize BER array
for i = 1:length(SNR)
    % generate random messages
    msg = randi([0 1], N, m);
    % generate random spreading sequences
    H = sqrt(0.5)*(randn(m,n)+1i*randn(m,n));
    % normalize spreading sequences
    H = H./sqrt(sum(abs(H).^2,2));
    % generate codewords for OMA
    x = zeros(N,n);
    for j = 1:N
        x(j,:) = sqrt(P)*msg(j,:)*H(j,:);
    end
    % generate noise
    sigma = sqrt(P./(SNR(i)*2*n));
    noise = sigma*(randn(N,n)+1i*randn(N,n));
    % generate received signal
    y = x + noise;
    % detection for OMA
    msg_hat = zeros(N,m);
    for j = 1:N
        [~,ind] = max(abs(y(j,:)*H').^2);
        msg_hat(j,:) = msg(ind,:);
    end
    % calculate BER for OMA
    BER(i) = sum(sum(msg~=msg_hat))/(N*m);
end
% plot BER vs SNR for OMA
semilogy(SNRdB,BER,'-o','linewidth',2);
grid on;
xlabel('SNR (dB)');
ylabel('BER');
title('BER vs SNR for OMA');