lfm1.m

matlab 自适应LFM源程序

%Conputer experiments on adaptive equalization  Experiment 2: LMS algorithm
clear;close all;clc;
N=1000;delta=7;
u3=[0.0075 0.025 0.075];
wk_mean=zeros(4,11);
err_mean=zeros(4,993);
for ii=1:3
    w=3.3;u=u3(ii);        % w the factor of amplitude distortion;u step length
    wk=zeros(100,11);        % wk=zeros(N,11);%Weight vector   ZEROS([M,N]) is an M-by-N matrix of zeros
    err=zeros(100,N-7);
    power_v=0.001;   
   hn=[0,0.5*(1+cos(2*pi/w*((1:3)-2)))];%The impulse response of the channel
   Am=sqrt(power_v);      % Power of additive gaussian white noise 
    for nn=1:100
        bernuli=randsrc(1,N);% Generate Bernoulli sequence   RANDSRC(M,N) generates an M-by-N random bipolar matrix                      1*1000
        vn=Am*randn(1,N+3);%Additive gaussian white noise   RANDN(M,N) and RANDN([M,N]) are M-by-N matrices with random entries          1*1003
        signal_in=[zeros(1,10),conv(bernuli,hn)+vn];%The input signal of adaptive transversal equalization   C = CONV(A, B) convolves vectors A and B.  The resulting.   1*1013
        bernuli_delta=[zeros(1,delta),bernuli];  %    1*1007
        for m=1:N-7
            alc_data=signal_in(m+10:-1:m);  %
            err(nn,m)=bernuli_delta(m)-alc_data*wk(nn,:)';  %
            wk(nn,:)=wk(nn,:)+u*err(nn,m)*signal_in(m+10:-1:m);  %
        end
    end
    wk_mean(ii,:)=sum(wk)/100;%
    err_mean(ii,:)=sum(err.*err)/100;%
end
subplot(3,1,1);
stem(wk_mean(1,:));title('u=0.0075');axis([0 11 -1 1.8]);grid on;
subplot(3,1,2);
stem(wk_mean(2,:));title('u=0.025');axis([0 11 -1 1.8]);grid on;
subplot(3,1,3);
stem(wk_mean(3,:));title('u=0.075');axis([0 11 -1 1.8]);grid on;
figure;
semilogy(1:993,err_mean(1,1:993),1:993,err_mean(2,1:993),1:993,err_mean(3,1:993));
xlabel('Number of iterations n');ylabel('Ensemble averange square error');
legend('u=0.0075','u=0.025','u=0.075');title('LMS algorithm');