LBER_and_MBER.rar_frontw2r_均衡 误比特率_波束扩展_误块率_随机梯度

%************************************************************************** % Learning curve of the LBER algorithm averaged over 40 iterations. % SNR=10 dB, SIRi=-6 dB for i=2,3,4,5 %************************************************************************** clear close all clc antenna=2; % number of arrays source=5; % number of sources theta=[15 -30 60 -70 80]; % theata is the angle of sources in digree iteration=40; M=500; % M is number of transmitted bits sigman=1/2^0.5; % 2sigman^2 is variance of noise MMSE_error=zeros(1,M); BER_error1=zeros(1,M); BER_error2=zeros(1,M); BER_error3=zeros(1,M); error_MMSE=zeros(1,M); error_BER1=zeros(1,M); error_BER2=zeros(1,M); error_BER3=zeros(1,M); for l=1:antenna for k=1:source % s(:,k) is steering vector for source k s(l,k)=exp(i*(l-1)*pi*sin(theta(k)*pi/180)); end end % A(i,i)^2 denotes th esignal power of source i SNR=10; A=10^((SNR+6)/20)*eye(source); A(1,1)=10^(SNR/20); P=s*A; % P=[A1s1 A2s2 ... Amsm] for l=1:iteration % each column of b is transmitted bit vector a=2*rand(5,M)-1; b=a./abs(a); % n is the comlex-valued white Gaussian noise n1=normrnd(0,sigman,[antenna,M]); n2=normrnd(0,sigman,[antenna,M]); n=n1+i*n2; W_MMSE=(P*P'+2*sigman^2*eye(antenna))^(-1)*P(:,1); for m=2:M y_MMSE(m,1)=W_MMSE'*(P*b(:,m)+n(:,m)); bp=real(y_MMSE(m,1))/abs(real(y_MMSE(m,1))); if( bp ~= b(1,m) ) error_MMSE(m)=(error_MMSE(m-1)*(m-1)+1)/m; end end MMSE_error=MMSE_error+error_MMSE; w1=zeros(antenna,M); w2=zeros(antenna,M); w3=zeros(antenna,M); Mu1=0.01; Mu2=0.05; Mu3=0.1; error=0; for m=2:M-1 x=P*b(:,m)+n(:,m); y=w1(:,m)'*x; w1(:,m+1)=w1(:,m)+Mu1*sign(b(1,m))/(2*(2*pi)^0.5*sigman)*... exp(-(real(y))^2/(2*sigman^2))*x; y=w2(:,m)'*x; w2(:,m+1)=w2(:,m)+Mu2*sign(b(1,m))/(2*(2*pi)^0.5*sigman)*... exp(-(real(y))^2/(2*sigman^2))*x; y=w3(:,m)'*x; w3(:,m+1)=w3(:,m)+Mu3*sign(b(1,m))/(2*(2*pi)^0.5*sigman)*... exp(-(real(y))^2/(2*sigman^2))*x; y_BER1(m,1)=w1(:,m+1)'*(P*b(:,m)+n(:,m)); y_BER2(m,1)=w2(:,m+1)'*(P*b(:,m)+n(:,m)); y_BER3(m,1)=w3(:,m+1)'*(P*b(:,m)+n(:,m)); bp1=real(y_BER1(m,1))/abs(real(y_BER1(m,1))); bp2=real(y_BER2(m,1))/abs(real(y_BER2(m,1))); bp3=real(y_BER3(m,1))/abs(real(y_BER3(m,1))); if( bp1 ~= b(1,m) ) error_BER1(m)=(error_BER1(m-1)*(m-1)+1)/m; end if( bp2 ~= b(1,m) ) error_BER2(m)=(error_BER2(m-1)*(m-1)+1)/m; end if( bp3 ~= b(1,m) ) error_BER3(m)=(error_BER3(m-1)*(m-1)+1)/m; end end BER_error1=BER_error1+error_BER1; BER_error2=BER_error2+error_BER2; BER_error3=BER_error3+error_BER3; end figure hold on title('Learning curve') xlabel('sample') ylabel('Bit Error Rate') m=1:M; plot(m,BER_error1./iteration,'g'); plot(m,BER_error2./iteration,'b'); plot(m,BER_error3./iteration,'y'); legend('u=0.01','u=0.05','u=0.1');