基本信息
源码名称：cma的仿真
源码大小：2.19KB
文件格式：.m
开发语言：MATLAB
更新时间：2021-09-07
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源码介绍
clc
clear all;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 实现了信号的盲均衡算法
% CMA、MCMA算法
%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%% 仿真条件 %%%%%%%%
i = sqrt(-1);
L = 6000;
SNR = 30;
h = [0.26-i*0.1, 0.93-i*0.2, 0.26];
h = h/norm(h);
Nh = length(h);
ISI_cma = 0;
ISI_mma = 0;
Nf = 17;
times = 20;
for loop = 1:times
M = 4;
iniphase = 0;
sa = randi([0 M-1],L,1);
% sn = (pskmod(sa,M,iniphase))';
% sn = (qammod(sa,M,iniphase))';
sn=pskmod(sa,M);
rn = filter(h,1,sn);
xn = awgn(rn,SNR,'measured');
Ri = mean(imag(sn).^4)/mean(imag(sn).^2);
Rr = mean(real(sn).^4)/mean(real(sn).^2);
R = mean(abs(sn).^4)/mean(abs(sn).^2);
%%%%%% CMA 算法 %%%%%%
W_cma = zeros(Nf,1);
W_cma((Nf 1)/2) = 1;
muCMA = 0.00005;
for k = 1:L-Nf
X_cma(:,k) = fliplr(xn(k:k Nf-1));
Y_cma(k) = W_cma'*X_cma(:,k);
E_cma(k) = Y_cma(k)*(abs(Y_cma(k))^2-R);
W_cma = W_cma - muCMA*conj(E_cma(k))*X_cma(:,k);
tmpx = conv(W_cma',h);
ISIx(k) = sum(abs(tmpx).^2)/max(abs(tmpx))^2-1;
ISIlgx(k) = 10*log10(ISIx(k));
end
ISI_cma = ISI_cma ISIlgx;
%%%%%% MCMA算法 %%%%%%%%%%
W_mma = zeros(Nf,1);
W_mma((Nf 1)/2) = 1;
% muMMA = 0.005;%%8PSK时的步长选取
muMMA = 0.00005;%%16QAM时的步长选取
for k = 1:L-Nf
X_mma(:,k) = fliplr(xn(k:k Nf-1));
Y_mma(k) = W_mma'*X_mma(:,k);
Yr = real(Y_mma(k));
Yi = imag(Y_mma(k));
Er_mma = Yr*(Yr^2-Rr);
Ei_mma = Yi*(Yi^2-Ri);
E_mma(k) = Er_mma i*Ei_mma;
W_mma = W_mma - muMMA*conj(E_mma(k))*X_mma(:,k);
tmpy = conv(W_mma',h);
ISIy(k) = sum(abs(tmpy).^2)/max(abs(tmpy))^2-1;
ISIlgy(k) = 10*log10(ISIy(k));
end
ISI_mma = ISI_mma ISIlgy;
end
ISI_cma = ISI_cma/times;
ISI_mma = ISI_mma/times;
figure
k = 1:length(ISI_cma);
plot(k,ISI_cma,k,ISI_mma,'r');
xlabel('迭代次数');ylabel('码间干扰ISI(dB)');
figure
subplot(2,2,1)
plot(xn,'.');title('均衡前');
subplot(2,2,2)
plot(Y_cma(500:end),'.');title('CMA算法');
subplot(2,2,3)
plot(Y_mma(500:end),'.');title('RLS-CMA算法');
clc
clear all;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 实现了信号的盲均衡算法
% CMA、MCMA算法
%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%% 仿真条件 %%%%%%%%
i = sqrt(-1);
L = 6000;
SNR = 30;
h = [0.26-i*0.1, 0.93-i*0.2, 0.26];
h = h/norm(h);
Nh = length(h);
ISI_cma = 0;
ISI_mma = 0;
Nf = 17;
times = 20;
for loop = 1:times
M = 4;
iniphase = 0;
sa = randi([0 M-1],L,1);
% sn = (pskmod(sa,M,iniphase))';
% sn = (qammod(sa,M,iniphase))';
sn=pskmod(sa,M);
rn = filter(h,1,sn);
xn = awgn(rn,SNR,'measured');
Ri = mean(imag(sn).^4)/mean(imag(sn).^2);
Rr = mean(real(sn).^4)/mean(real(sn).^2);
R = mean(abs(sn).^4)/mean(abs(sn).^2);
%%%%%% CMA 算法 %%%%%%
W_cma = zeros(Nf,1);
W_cma((Nf 1)/2) = 1;
muCMA = 0.00005;
for k = 1:L-Nf
X_cma(:,k) = fliplr(xn(k:k Nf-1));
Y_cma(k) = W_cma'*X_cma(:,k);
E_cma(k) = Y_cma(k)*(abs(Y_cma(k))^2-R);
W_cma = W_cma - muCMA*conj(E_cma(k))*X_cma(:,k);
tmpx = conv(W_cma',h);
ISIx(k) = sum(abs(tmpx).^2)/max(abs(tmpx))^2-1;
ISIlgx(k) = 10*log10(ISIx(k));
end
ISI_cma = ISI_cma ISIlgx;
%%%%%% MCMA算法 %%%%%%%%%%
W_mma = zeros(Nf,1);
W_mma((Nf 1)/2) = 1;
% muMMA = 0.005;%%8PSK时的步长选取
muMMA = 0.00005;%%16QAM时的步长选取
for k = 1:L-Nf
X_mma(:,k) = fliplr(xn(k:k Nf-1));
Y_mma(k) = W_mma'*X_mma(:,k);
Yr = real(Y_mma(k));
Yi = imag(Y_mma(k));
Er_mma = Yr*(Yr^2-Rr);
Ei_mma = Yi*(Yi^2-Ri);
E_mma(k) = Er_mma i*Ei_mma;
W_mma = W_mma - muMMA*conj(E_mma(k))*X_mma(:,k);
tmpy = conv(W_mma',h);
ISIy(k) = sum(abs(tmpy).^2)/max(abs(tmpy))^2-1;
ISIlgy(k) = 10*log10(ISIy(k));
end
ISI_mma = ISI_mma ISIlgy;
end
ISI_cma = ISI_cma/times;
ISI_mma = ISI_mma/times;
figure
k = 1:length(ISI_cma);
plot(k,ISI_cma,k,ISI_mma,'r');
xlabel('迭代次数');ylabel('码间干扰ISI(dB)');
figure
subplot(2,2,1)
plot(xn,'.');title('均衡前');
subplot(2,2,2)
plot(Y_cma(500:end),'.');title('CMA算法');
subplot(2,2,3)
plot(Y_mma(500:end),'.');title('RLS-CMA算法');