dferls_equation.asv

就是自适应的一写程序

function [data_estimation,data_decision,C]=dferls_equation(K1,K2,signal_in,signal_original)
%基于数字通信书的P495页的算法
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%                          训练模式                                   %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
N=K1+K2+1;
%均衡滤波器的阶数
w=0.999;
%加权因子
C=zeros(N,1);
%均衡滤波器各抽头的系数初始化为零
Num=length(signal_in)-N+1;
%由于反馈的引入,实际对Num个数据进行了均衡
I=zeros(Num,1);
%均衡后的结果，尚未判决
error=zeros(Num,1); 
%误差矩阵
Yall=zeros(N,Num);
%每一列代表每次均衡所用的数据，其中前K1+1个是接收到的值，后K2个是判决反馈后的值,在训练模式下,即为已知值
Call=zeros(N,Num);
%每列代表每次迭代所得到的滤波器的系数的值
P=1000*eye(N) ; 
%R矩阵的逆,R矩阵初值取deltaI，取delta为0.001
train_length=length(signal_original);
for t=K2+1:train_length
    Yall(:,t)=[signal_in(K1+t:-1:t);signal_original(t-1:-1:t-K2)];
    %第t次迭代对应Y矩阵的第t列，每列元素的前K1+1个为接收到的信号，后K2个为已知序列，
    Y=Yall(:,t);
    %代表第t次迭代用到的数据
    Y_star=conj(Y);
    %取共轭
    Y_trans=Y.';
    %取转置
    I(t)=Y_trans*C; 
    %计算均衡输出
    error(t)=signal_original(t)-I(t);
    %计算误差
    K=(P*Y_star)/(w+(Y_trans*P*Y_star));
    %计算卡尔曼增益
    P=(1/w)*(P-(K*Y_trans*P));
    %更新矩阵P
    C=C+(error(t)*K);
    %更新均衡器的系数矩阵
    Call(:,t)=C;
end
semilogy(power(abs(error),2))
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%                          传输模式                                   %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Num=length(signal_in)-train_length-K1-1;%所传输的信息序列的长度
data_decision=zeros(Num,1);
data_estimation=zeros(Num,1);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%前K2
data(:,1)=[signal_in(train_length+K1+1:-1:train_length+1);signal_original(train_length:-1:train_length-K2+1)];
data_estimation(1)= data(:,1).'*C;
data_decision(1)=decision(data_estimation(1));
for t=2:K2
    data(:,t)=[signal_in(train_length+K1+t:-1:train_length+t);data_decision(t-1:-1:1);signal_original(train_length:-1:train_length-K2+t)];
    data_estimation(t)= data(:,t).'*C;
    data_decision(t)=decision(data_estimation(t));
end
Y=zeros(N,Num);
for t=(K2+1):Num
     for n=1:K1+1
        data(n,t)=signal_in(train_length+t+K1-n+1);
     end;
     for n=(K1+2):(K1+K2+1)
        data(n,t)=data_decision(t+K1-n+1);
     end 
     data_estimation(t)=C.'*data(:,t);
     data_decision(t)=decision(data_estimation(t));
end