CMA.rar_CMA QAM_SteeringVector_cma_cma-isi algorithm_constant m

N=2; SNR=30; sType='c'; IntType(1)='r'; IntType(2)='r'; nType='g'; mu=0.0009; lambda=0.15; d=lambda/2; nSamples=100; Theta_S=rand(1,nSamples)*60-60*rand(1,nSamples); I=rand(1,nSamples)*60-60*rand(1,nSamples); Q=rand(1,nSamples)*60-60*rand(1,nSamples); for h=1:nSamples Theta_I(1,h)=I(h); Theta_I(2,h)=Q(h); end; SIR=[0 0]; k = 2*pi/lambda; Theta_S = Theta_S*pi/180; Theta_I = Theta_I*pi/180; SignalPower = 1; N_Int = length(SIR); % ------------- Signal ------------------% % Steering Vector. ApV_S = zeros(N,nSamples); for index = 1:nSamples, ApV_S(:,index) = SteeringVector(N,k,d,Theta_S(index))'; % 0 <= n <= N-1. end D_n = GenSigAdaptive(sType,SignalPower,nSamples,ApV_S); N_n = GenSigAdaptive(nType,10^(-SNR/10),nSamples,ApV_S); S_n = D_n + N_n; % ---------- Interferers ---------------% I_Total = zeros(N,nSamples); for index = 1:N_Int, SV = zeros(N,nSamples); for timeIndex = 1:nSamples SV(:,timeIndex) = SteeringVector(N,k,d,Theta_I(index,timeIndex))'; end I_n = GenSigAdaptive(IntType(index),10^(-SIR(index)/10),nSamples,SV); I_Total = I_Total + I_n; end; % Rx Signal = Signal + Inteference. X_n = S_n + I_Total; % ------------ Weights ----------------------------% W = ones(N,nSamples); % ----------Desired Signal ------------------------% Desired = D_n(1,:); % --------- LMS Filtering Part --------------------% Error = zeros(1,nSamples); nErrors=0; a=0; for index = 1:nSamples, y(index)=X_n(:,index)'*W(:,index) Error(index) = y(index) - y(index)/abs(y(index)); W(:,index+1) = W(:,index)-mu*Error(index)*X_n(:,index); end %nErrors=sum(X_n(1,:)'*W(1,:)~=Desired'); BER=nErrors/nSamples; LC=conj(Error).*Error; x=1:nSamples; plot(1:nSamples,LC);