1.软件版本
matlab2017b
2.系统概述
clc;
clear all;
close all;
warning off;
addpath 'func\'
rng(1)
N = 300000;%指定信号序列长度?
info = func_random_binary(N);%产生二进制信号序列
SNR_in_dB=[7:1:14];%AWGN信道信噪比
% SNR_in_dB=[12];%AWGN信道信噪比
for j=1:length(SNR_in_dB)
j
%通过既有码间干扰又有白噪声信道
[y,len,h] = func_channel(info,SNR_in_dB(j));
%初始误码统计数
numoferr1=0;
%从第len个码元开始为真实信号码元
for i=len+1:N+len
decis = 2*[y(i)>=0]-1;
%判断是否误码，统计误码码元个数
if decis~=info(i-5)
numoferr1=numoferr1+1;
end;
end;
%未经均衡器均衡，得到的误码率
Pe1(j)=numoferr1/N;
%LS均衡,参考文献
%https://wenku.baidu.com/view/3fb6f52f195f312b3069a5a6.html
Order = 5;
z = func_LS(y,info,Order);
%初始误码统计数
numoferr2=0;
%从第len个码元开始为真实信号码元
for i=1:N
decis(i) = 2*[z(i+Order)>=0]-1;
%判断是否误码，统计误码码元个数
if decis(i)~=info(i)
numoferr2=numoferr2+1;
end;
end;
Pe2(j)=numoferr2/N;
%DEF均衡
z = func_DEF(y(6:length(info)+5),info,h);
decis = [2*[z>=0]-1];
%初始误码统计数
numoferr3=0;
%从第len个码元开始为真实信号码元
for i=1:N
if decis(i)~=info(i)
numoferr3=numoferr3+1;
end;
end;
Pe3(j)=numoferr3/N;
%LMMSE均衡
z = func_LMMSE(y,h,SNR_in_dB(j));
decis = [2*[z>=0]-1]';
%初始误码统计数
numoferr4=0;
%从第len个码元开始为真实信号码元
for i=1:N-5
if decis(i+5)~=info(i)
numoferr4=numoferr4+1;
end;
end;
Pe4(j)=numoferr4/N;
end;
figure;
semilogy(SNR_in_dB,Pe1,'red*-');
hold on;
semilogy(SNR_in_dB,Pe2,'b-s');
hold on;
semilogy(SNR_in_dB,Pe3,'k--','linewidth',2);
hold on;
semilogy(SNR_in_dB,Pe4,'m->','linewidth',2);
grid on
legend('无均衡器','LS均衡器','DEF均衡器','LMMSE均衡器');
xlabel('SNR(dB)');
ylabel('error');
function y=func_LS(x,I,Order);
%LS均衡,参考文献
%https://wenku.baidu.com/view/3fb6f52f195f312b3069a5a6.html
%根据LS方法计算权值w
r = x; % output of channel
n = Order; % rank of equalizer 均衡器的长度
delta = Order; % delay 信道延迟
p = length(r)-delta;
A = toeplitz(r(n+1:p),r(n+1:-1:1));
b = I(n+1-delta:p-delta)';
w = inv(A'*A)*A'*b; %均衡器系数
y = conv(w,r);
function s = func_DEF(x,info,H);
M = length(x);
L = 27;
a = 0.00005;
d = 5;
M2= M;
xf= x(1:M2);
for Iter=1:20;
s = zeros(1,length(xf));
y = xf;
W1 = zeros(1,L);
W2 = zeros(1,d);
for m= d+L:1:M2
sum = 0;
sum1 = 0;
sum2 = 0;
for n=m-1:-1:m-L
sum1=sum1+y(n)*W1(m-n);
end
for n=1:d
sum2=sum2+s(m-d-n)*W2(n);
end
e=info(m-d)-sum1+sum2;
for n=m-1:-1:m-L
W1(m-n)=W1(m-n)+2*a*e*y(n);
end
for n=1:d
W2(n)=W2(n)-2*a*e*s(m-d-n);
end
sum = sum1-sum2;
if sum >= 0
sum = 1;
else
sum =-1;
end
s(m-d)=sum;
end
end
for m= d+L:1:M2
sum = 0;
sum1 = 0;
sum2 = 0;
for n=m-1:-1:m-L
sum1=sum1+y(n)*W1(m-n);
end
for n=1:d
sum2=sum2+s(m-d-n)*W2(n);
end
e=info(m-d)-sum1+sum2;
for n=m-1:-1:m-L
W1(m-n)=W1(m-n)+2*a*e*y(n);
end
for n=1:d
W2(n)=W2(n)-2*a*e*s(m-d-n);
end
sum = sum1-sum2;
if sum >= 0
sum = 1;
else
sum =-1;
end
s(m-d)=sum;
end
function y = func_LMMSE(x,h,SNR);
N0 = 10^(-SNR/10);
sigma = sqrt(N0);
Len = 2500;
Lens = floor(length(x)/Len);
Order = length(h);
y = [];
for j = 1:Lens
Xtmp = x((j-1)*Len+1:j*Len);
H1 = zeros(Len,Len+Order-1);
for k=1: Len
H1(k,k:k+Order-1) = fliplr(h);
end
H=H1(:,round(Order/2):Len+round(Order/2)-1);
Xor = xcorr(Xtmp,Xtmp)/Len;
Xor = fliplr(Xor);
L_Xtmp = round(length(Xor)/2);
C_yy = zeros(L_Xtmp,L_Xtmp);
for i=1:L_Xtmp;
C_yy(i,:) = Xor(L_Xtmp+1-i:length(Xor)+1-i);
end
C_v = sigma^2*eye(Len);
C_xy = H\(C_yy-C_v);
E_yk = sum(x)/Len*ones(1,Len);
E_y = H\E_yk';
y_LMMSE = E_y + C_xy/C_yy*(Xtmp-E_yk)';
y = [y;y_LMMSE];
end3.仿真结论
4.参考文献
[1]聂晓鸿, 王建. 最小二乘法自适应均衡滤波研究[J]. 信息技术, 2013, 37(12):3.A01-139