Continue to Site

Welcome to EDAboard.com

Welcome to our site! EDAboard.com is an international Electronics Discussion Forum focused on EDA software, circuits, schematics, books, theory, papers, asic, pld, 8051, DSP, Network, RF, Analog Design, PCB, Service Manuals... and a whole lot more! To participate you need to register. Registration is free. Click here to register now.

Looking for Matlab simulations of MIMO for CDMA

Status
Not open for further replies.

mujee

Member level 1
Joined
Feb 11, 2007
Messages
37
Helped
0
Reputation
0
Reaction score
0
Trophy points
1,286
Activity points
1,585
hey guys
i need matlab simulations of mimo for cdma. if anyone have it please upload it as i need to submit my results very soon and these codes would help me finish my report in time
 

Re: mimo code

no help :( .....cant believe nobody has done cdma in mimo.....i dont have time otherwise i would have done it myself....i just need graphs...i tried looking it on mathworks too but couldnt find it there
is there anyone who could help?
 

mimo code

Hi, try this program:


clear all; close all;


N=20; % number of users
M=20; % spreading gain
L=4; % channel order
Z=4; % number of antenna
Q=M-L;
%Symbols=500;% no of data vectors
cx=hadamard(2*N);
cn=cx(2:N+1,1:N);clear cx;
SNR=25; % signal to noise ratio in dB

%%% autocorrelation matrix of the spreading code
Rcc=cn*cn';

po=51;%number of points for the plot
nj=50:5:300;
% po=4;%number of points for the plot
% nj=50:50:200;
%nj=[50 100 150 200 250 300];%size of data vectors
% n=200;%number of generated symbols

ni=po;%number of iterations
%var=logspace(-3,-1,ni);%noise variance
var=0.07;
sig=sqrt(var);

%%% parameters for the adaptive algorithms
beta=0.05;gm=0.4;

% Difference=0;
%====================
%Channel
%====================

chn=[
-0.0490+0.3590i
0.4820-0.5690i
-0.5560+0.5870i
1.0000
-0.1710+0.0610i
0.4430-0.0364i
1.0000
0.9210-0.1940i
0.1890-0.2080i
-0.0870-0.0540i
-0.2110-0.3220i
-0.1990+0.9180i
1.0000
-0.2840-0.5240i
0.1360-0.1900i
0.4170+0.0300i
1.0000
0.8730 + 0.1450i
0.2850 + 0.3090i
-0.0490 + 0.1610i];

% channel r=1
c1=[-0.049+0.359i 0.482-0.569i -0.556+0.587i 1 -0.171+0.061i zeros(1,M-L-1)];
r1=[-0.049+0.359i zeros(1,M-1)];
H1=toeplitz(c1,r1);
% channel r=2
c2=[0.443-0.0364i 1 0.921-0.194i 0.189-0.208i -0.087-0.054i zeros(1,M-L-1)];
r2=[0.443-0.0364i zeros(1,M-1)];
H2=toeplitz(c2,r2);
% channel r=3
c3=[-0.211-0.322i -0.199+0.918i 1 -0.284-0.524i 0.136-0.190i zeros(1,M-L-1)];
r3=[-0.211-0.322i zeros(1,M-1)];
H3=toeplitz(c3,r3);
%channel r=4
c4=[0.417+0.030i 1 0.873+0.145i 0.285+0.309i -0.049+0.161i zeros(1,M-L-1)];
r4=[0.417+0.030i zeros(1,M-1)];
H4=toeplitz(c4,r4);

R=[zeros(M-L,L) eye(M-L)];%to generate truncate matrix
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%% repeat the process for a variety of SNR
K=50;%the number of averaging= block size
snr=zeros(1,ni);
mci=50;%no of MC runs
er1=zeros(ni,mci); er2=er1; msx=er1;
mse=zeros(ni,1);nm=mse;
it=1;

for p=1:ni,
for mc=1:mci,%100 Monte Carlo runs

fprintf('%d th MC run\n',it);it=it+1;
n=nj(p);
he=zeros(4*(L+1),n);
szf=zeros(1,n);%detected transmitted symbols
smm=zeros(1,n);%detected transmitted symbols
u=zeros(M,n);

s=randsrc(N,n);%generate tx BPSK symbols
u=cn*s;%size M x n
Ruu=zeros(M);
%generate correlation matrix
for m=1:K,
Ruu=Ruu+[u:),m)*u:),m)'];
end;
Ruu=Ruu/K;
%%%%%%%%%%%%%%%%%%%%%%%%%% 1st antenna %%%%%%%%%%%%%%%%%%%%%%%%
n1=sig*(1/sqrt(2))*[randn(Q,n)+sqrt(-1)*randn(Q,n)];%generate random noise

x1=(R*H1*cn*s)+n1;%received vector for the 1st antenna, size of Q x n

%%%%%%%%%%%%%%%%%%%%%%%%%% 2nd antenna %%%%%%%%%%%%%%%%%%%%%%%%
n2=sig*(1/sqrt(2))*[randn(Q,n)+sqrt(-1)*randn(Q,n)];%generate random noise

x2=(R*H2*cn*s)+n2;%received vector for the 2nd antenna, size of Q x n

%%%%%%%%%%%%%%%%%%%%%%%%%% 3rd antenna %%%%%%%%%%%%%%%%%%%%%%%%
n3=sig*(1/sqrt(2))*[randn(Q,n)+sqrt(-1)*randn(Q,n)];%generate random noise

x3=(R*H3*cn*s)+n3;%received vector for the 3rd antenna, size of Q x n

%%%%%%%%%%%%%%%%%%%%%%%%%% 4th antenna %%%%%%%%%%%%%%%%%%%%%%%%
n4=sig*(1/sqrt(2))*[randn(Q,n)+sqrt(-1)*randn(Q,n)];%generate random noise

x4=(R*H4*cn*s)+n4;%received vector for the 4th antenna, size of Q x n


%%%%%%%%%%%%%%
x=[x1.' x2.' x3.' x4.'].';%stack all received vectors from the 4 antennas, size 4Q x n

Nos=sum(sum((abs(n1).^2),1))/(Q*n);%average noise power
sn=sum(sum((abs(x1-n1).^2),1))/(n*N);%energy per-bit, BPSK= 1 bit per-symbol
snr(p)=10*log10(sn/Nos);

%%%%% Blind Subspace Channel Estimation %%%%%%%%%%
Rxx=zeros(4*Q,4*Q);
%generate autocorrelation matrix
for m=1:K,
Rxx=Rxx+[x:),m)*x:),m)'];
end;
Rxx=Rxx/K;
[V,D]=eig(Rxx);%eigen value decomposition, size of V and D is 4Q x 4Q
Gn=V:),1:(4*Q)-M);%noise subspace
Gj1=zeros(L+1,M*((4*Q)-M));Gj2=Gj1;Gj3=Gj1;Gj4=Gj1;
Gj=zeros(Z*(L+1),M*((4*Q)-M));
%%%%%%%%
c=0;
for k=1:(4*Q)-M,
b=0;
%%% noise subspace from the first antenna
for i=1:L+1,
a=Q;
for j=1:Q, Gj1(i,j+b+c)=Gn(a,k); a=a-1; end;
b=b+1;
end;
b=0;
%%% noise subspace from the 2nd antenna
for i=1:L+1,
a=2*Q;
for j=1:Q, Gj2(i,j+b+c)=Gn(a,k); a=a-1; end;
b=b+1;
end;
b=0;
%%% noise subspace from the third antenna
for i=1:L+1,
a=3*Q;
for j=1:Q, Gj3(i,j+b+c)=Gn(a,k); a=a-1; end;
b=b+1;
end;
b=0;
%%% noise subspace from the fourth antenna
for i=1:L+1,
a=4*Q;
for j=1:Q, Gj4(i,j+b+c)=Gn(a,k); a=a-1; end;
b=b+1;
end;

c=c+M;
end;

Gj=[Gj1.' Gj2.' Gj3.' Gj4.'].';%size Z*(L+1) x M*((4*Q)-M)
Qx=zeros(Z*(L+1));clear a;a=1:M;
for i=1:(4*Q)-M, Qx=Qx+(Gj:),a)*cn*cn'*Gj:),a)');a=a+M;end;
%%%%%% Obtaining channel estimate, hest
[hest,A]=eigs(Qx,1,'SM');%return eigenvector corresponds to the minimum eigenvalue of Q, size 4(L+1) x 1
hest=hest*(c1(1)/hest(1));%phase ambiguity removal

%%%%%%%%%% Blind Adaptive Subspace Tracking Algorithm %%%%%%%%%%%
%compute the channel estimate for the whole tx symbols
Hq1=zeros(Q,M);Hq2=Hq1;Hq3=Hq1;Hq4=Hq1;HQ=zeros(4*Q,M);

for m=1:n,
%NOOJA Algorithm
%noise subspace, algorithm at i interval
fn=Gn'*x:),m);
gn=Gn*fn;
pn=x:),m)-gn;
bop=((beta)/[norm(x:),m))-norm(fn)+gm]);
phi=1/sqrt(1+(bop^2*norm(pn)*norm(fn)));
tau=(phi-1)/norm(fn);
pi=((-tau*gn)/bop)+(phi*pn);
chi=pi/norm(pi,1);
ka=Gn'*chi;
Gnn=Gn-(2*chi*ka');%noise subspace for the next symbol

%%%%%%%% construct matrix Q based on the estimate Gn
c=M;
for k=1:(4*Q)-M,
b=0;
%%% noise subspace from the first antenna
for i=1:L+1,
a=Q;
for j=1:Q, Gj1(i,j+b+c)=Gnn(a,k); a=a-1; end;
b=b+1;
end;
b=0;
%%% noise subspace from the 2nd antenna
for i=1:L+1,
a=2*Q;
for j=1:Q, Gj2(i,j+b+c)=Gnn(a,k); a=a-1; end;
b=b+1;
end;
b=0;
%%% noise subspace from the third antenna
for i=1:L+1,
a=3*Q;
for j=1:Q, Gj3(i,j+b+c)=Gnn(a,k); a=a-1; end;
b=b+1;
end;
b=0;
%%% noise subspace from the fourth antenna
for i=1:L+1,
a=4*Q;
for j=1:Q, Gj4(i,j+b+c)=Gnn(a,k); a=a-1; end;
b=b+1;
end;

c=c+M;
end;

Gj=[Gj1.' Gj2.' Gj3.' Gj4.'].';%size Z*(L+1) x M*((4*Q)-M)
Qx=zeros(Z*(L+1));clear a;a=1:M;
for i=1:(4*Q)-M, Qx=Qx+(Gj:),a)*cn*cn'*Gj:),a)');a=a+M;end;

%%%% compute the channel estimate for the next symbol
mu=0.0001;
ben=hest'*Qx*hest;
hen=hest-(mu*[(norm(hest)*Qx)-(ben*eye(4*(L+1)))]*hest);%the updated channel estimate

%%%% store all channel estimates into he
if m==1, he:),m)=hest; else he:),m)=hen; end;

%%%%%% Construct the Hq matrix for each antenna %%%%%%%
b=0;
%%% the first antenna
for i=1:Q,
a=L+1;
for j=1:L+1, Hq1(i,j+b)=he(a,m); a=a-1; end;
b=b+1;
end;
b=0;
%%% the 2nd antenna
for i=1:Q,
a=2*(L+1);
for j=1:L+1, Hq2(i,j+b)=he(a,m); a=a-1; end;
b=b+1;
end;
b=0;
%%% the third antenna
for i=1:Q,
a=3*(L+1);
for j=1:L+1, Hq3(i,j+b)=he(a,m); a=a-1; end;
b=b+1;
end;
b=0;
%%% the fourth antenna
for i=1:Q,
a=4*(L+1);
for j=1:L+1, Hq4(i,j+b)=he(a,m); a=a-1; end;
b=b+1;
end;

HQ=[Hq1.' Hq2.' Hq3.' Hq4.'].';%the channel estimates, size 4Q x M

%%%%%%%%%% Equalisation using ZF and MMSE equalisers
Gzf=inv(HQ'*HQ)*HQ';%ZF, size M x 4Q
% clear tmp;tmp=Gzf;Gzf=tmp.';%change the matrix orientation

% Rxx=zeros(4*Q,4*Q);
% %generate autocorrelation matrix
% for m=1:K,
% Rxx=Rxx+[x:),m)*x:),m)'];
% end;
% Rxx=Rxx/K;
% Gmmse=Rux*inv(Rxx);%mmse
Gmmse=Ruu*HQ'*pinv(Rxx);%mmse, size M x 4Q

%%%Detection for the 1st user
%%%using ZF detector/equaliser
sx:),m)=sign(real(cn'*Gzf*x:),m)));%size M x n
%%%using MMSE detector/equaliser
sy:),m)=sign(real(cn'*Gmmse*x:),m)));%size M x n

end;%end of loop for m

%%%% obtain the estimated transmitted data for user 1
szf=sx(1,:);
smm=sy(1,:);

%%% to calculate the BER
%ZF
for k=1:n,
if szf(k)==s(1,k), b=0;
else b=1;
end;
er1(p,mc)=er1(p,mc)+b;
end;
er1(p,mc)=er1(p,mc)/n;

%MMSE
for k=1:n,
if smm(k)==s(1,k), b=0;
else b=1;
end;
er2(p,mc)=er2(p,mc)+b;
end;
er2(p,mc)=er2(p,mc)/n;

%%% compute NRMSE
clear tmp; tmp=0;
for i=1:n, tmp=tmp+norm(he:),i)-chn); end;
msx(p,mc)=tmp/n;%MSE

end;%end of 'mc' loop
mse(p)=mean(msx(p,:));
nm(p)=(1/norm(chn))*sqrt(mse(p));%NRMSE
end;%end of 'p' loop

% figure;
% semilogy(snr,er1,'-*',snr,er2,'-o');
% xlabel('SNR (dB)');
% ylabel('BER');
% legend('ZF','MMSE');
% title('Performance of the Detectors for the DS-CDMA');
%
% figure;
% semilogy(snr,mse,'-*');
% xlabel('SNR (dB)');
% ylabel('MSE');
% legend('DP');
% title('Performance of the Channel Estimator for the DS-CDMA');

save dat5 nj nm;

figure;
plot(nj,nm,'-o');
xlabel('Number of iterations');
ylabel('NRMSE');
legend('DP');
title('Performance of the Channel Estimator for the DS-CDMA');
 

    V

    Points: 2
    Helpful Answer Positive Rating
Re: mimo code

thanks for your code.while i tried to implement your coding in my matlab7.5 it is showing error.can you help plz.this is the error shown
Error: File: mimosample.m Line: 140 Column: 15
Unbalanced or unexpected parenthesis or bracket.
 

Re: mimo code

here are some basics codes for mimo system

frmLen = 100; % frame length
maxNumErrs = 300; % maximum number of errors
maxNumPackets = 3000; % maximum number of packets
EbNo = 0:2:12; % Eb/No varying to 12 dB
N = 2; % number of Tx antennas
M = 2; % number of Rx antennas
pLen = 8; % number of pilot symbols per frame
W = hadamard(pLen);
pilots = W:), 1:N); % orthogonal set per transmit antenna

demod22 = demodulate(bpskdemod, sum(z, 2)); % known

% Determine errors
numPackets = numPackets + 1;
totNumErr22_e = totNumErr22_e + biterr(demod22_e, data);
totNumErr22 = totNumErr22 + biterr(demod22, data);
end % end of FOR loop for numPackets

% Calculate BER for current idx
% for estimated channel
BER22_e(idx) = totNumErr22_e/(numPackets*frmLen);

% for known channel
BER22(idx) = totNumErr22/(numPackets*frmLen);

% Plot results
semilogy(EbNo(1:idx), BER22_e(1:idx), 'ro');
semilogy(EbNo(1:idx), BER22(1:idx), 'g*');
legend(['Channel estimated with ' num2str(pLen) ' pilot symbols/frame'],...
'Known channel');
drawnow;
end % end of for loop for EbNo

% Perform curve fitting and replot the results
fitBER22_e = berfit(EbNo, BER22_e);
fitBER22 = berfit(EbNo, BER22);
semilogy(EbNo, fitBER22_e, 'r', EbNo, fitBER22, 'g'); hold off;

Hope this will help
 

Status
Not open for further replies.

Similar threads

Part and Inventory Search

Welcome to EDABoard.com

Sponsor

Back
Top