LMS盲信道估计QPSK仿真：基于Matlab的盲估计方法

1.软件版本

MATLAB2021a

2.核心代码

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% CHANNEL EQUALIZATION USING LMS clc;clear all;close all;M=3000;    % number of data samplesT=2000;    
% number of training symbolsdB=25;     % SNR in dB valueL=20; % length for smoothing(L+1)ChL=5;  
% length of the channel(ChL+1)EqD=round((L+ChL)/2);  %delay for equalizationCh=randn(1,ChL+1)+sqrt(-1)*
randn(1,ChL+1);   % complex channelCh=Ch/norm(Ch);                     
% scale the channel with normTxS=round(rand(1,M))*2-1;  
% QPSK transmitted sequenceTxS=TxS+sqrt(-1)*(round(rand(1,M))*2-1);x=filter(Ch,1,TxS);  
%channel distortionn=randn(1,M);  %+sqrt(-1)*randn(1,M);   
%Additive white gaussian noise n=n/norm(n)*10^(-dB/20)*norm(x);  
% scale the noise power in accordance with SNRx=x+n;                           
% received noisy signalK=M-L;   
%% Discarding several starting samples for avoiding 0's and negativeX=zeros(L+1,K);  
% each vector column is a samplefor i=1:K    X(:,i)=x(i+L:-1:i).';
end%adaptive LMS Equalizere=zeros(1,T-10);  % initial errorc=zeros(L+1,1);   
% initial conditionmu=0.001;        % step sizefor i=1:T-10    e(i)=TxS(i+10+L-EqD)-c'*X(:,i+10);   
% instant error    c=c+mu*conj(e(i))*X(:,i+10);           
% update filter or equalizer coefficientendsb=c'*X;   
% recieved symbol estimation%SER(decision part)sb1=sb/norm(c);  
% normalize the outputsb1=sign(real(sb1))+sqrt(-1)*sign(imag(sb1));  
%symbol detectionstart=7;  sb2=sb1-TxS(start+1:start+length(sb1));  
% error detectionSER=length(find(sb2~=0))/length(sb2); 
%  SER calculationdisp(SER);% plot of transmitted symbols    subplot(2,2,1),     plot(TxS,'*');       
grid,title('Input symbols');  xlabel('real part'),ylabel('imaginary part')    axis([-2 2 -2 2])    
% plot of received symbols    subplot(2,2,2),    plot(x,'o');    grid, title('Received samples');  
xlabel('real part'), ylabel('imaginary part')% plots of the equalized symbols        subplot(2,2,3),    
plot(sb,'o');       grid, title('Equalized symbols'), xlabel('real part'), ylabel('imaginary part')
% convergence    subplot(2,2,4),    plot(abs(e));       grid, title('Convergence'), xlabel('n'), 
ylabel('error signal')    %%        %IMPLEMENTATION OF BLIND CHANNEL USING CMA OR GODARD ALGORITHM 
IMPLEMENTEDclc;clear all;close all;N=3000;    % number of sample datadB=25;     % Signal to noise 
ratio(dB)L=20; % smoothing length L+1ChL=1;  % length of the channel= ChL+1EqD=round((L+ChL)/2);  
%  channel equalization delayi=sqrt(-1);%Ch=randn(1,ChL+1)+sqrt(-1)*randn(1,ChL+1);   
% complex channel%Ch=[0.0545+j*0.05 .2832-.1197*j -.7676+.2788*j -.0641-.0576*j .0566-.2275*j .
4063-.0739*j];Ch=[0.8+i*0.1 .9-i*0.2]; %complex channel    Ch=Ch/norm(Ch);% normalizeTxS=round(rand(1,N))*2-1;
% QPSK symbols are transmitted symbolsTxS=TxS+sqrt(-1)*(round(rand(1,N))*2-1);x=filter(Ch,1,TxS); 
%channel distortionn=randn(1,N)+sqrt(-1)*randn(1,N);   
% additive white gaussian noise (complex) n=n/norm(n)*10^(-dB/20)*norm(x);  
% scale noise powerx1=x+n;  % received noisy signal...................1.2.3.4.5.6.7.8.9.10.11.12.
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