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In a previous post, we had discussed a 2×2 MIMO transmission using BPSK modulation in Rayleigh channel with a Zero Forcing equalizer. The simulated results with the 2×2 MIMO system with zero forcing equalizer showed matching results as obtained in for a 1×1 system for BPSK modulation in Rayleigh channel. In this post, we will discuss a different equalization approach called Minimum Mean Square Error (MMSE) equalization. We will assume that the channel is a flat fading Rayleigh multipath channel and the modulation is BPSK.
The background material on the MIMO channel has been described in the post on Zero Forcing equalizer. The text is repeated again for easy readability.
2×2 MIMO channel
In a 2×2 MIMO channel, probable usage of the available 2 transmit antennas can be as follows:
1. Consider that we have a transmission sequence, for example
2. In normal transmission, we will be sending in the first time slot,
in the second time slot,
and so on.
3. However, as we now have 2 transmit antennas, we may group the symbols into groups of two. In the first time slot, send and
from the first and second antenna. In second time slot, send
and
from the first and second antenna, send
and
in the third time slot and so on.
4. Notice that as we are grouping two symbols and sending them in one time slot, we need only time slots to complete the transmission – data rate is doubled !
5. This forms the simple explanation of a probable MIMO transmission scheme with 2 transmit antennas and 2 receive antennas.
Figure: 2 Transmit 2 Receive (2×2) MIMO channel
Other Assumptions
1. The channel is flat fading – In simple terms, it means that the multipath channel has only one tap. So, the convolution operation reduces to a simple multiplication. For a more rigorous discussion on flat fading and frequency selective fading, may I urge you to review Chapter 15.3 Signal Time-Spreading from [DIGITAL COMMUNICATIONS: SKLAR]
2. The channel experience by each transmit antenna is independent from the channel experienced by other transmit antennas.
3. For the transmit antenna to
receive antenna, each transmitted symbol gets multiplied by a randomly varying complex number
. As the channel under consideration is a Rayleigh channel, the real and imaginary parts of
are Gaussian distributed having mean
and variance
.
4. The channel experienced between each transmit to the receive antenna is independent and randomly varying in time.
5. On the receive antenna, the noise has the Gaussian probability density function with
with
and
.
7. The channel is known at the receiver.
Minimum Mean Square Error (MMSE) equalizer for 2×2 MIMO channel
Let us now try to understand the math for extracting the two symbols which interfered with each other. In the first time slot, the received signal on the first receive antenna is,
.
The received signal on the second receive antenna is,
.
where
,
are the received symbol on the first and second antenna respectively,
is the channel from
transmit antenna to
receive antenna,
is the channel from
transmit antenna to
receive antenna,
is the channel from
transmit antenna to
receive antenna,
is the channel from
transmit antenna to
receive antenna,
,
are the transmitted symbols and
is the noise on
receive antennas.
We assume that the receiver knows ,
,
and
. The receiver also knows
and
. For convenience, the above equation can be represented in matrix notation as follows:
.
Equivalently,
The Minimum Mean Square Error (MMSE) approach tries to find a coefficient which minimizes the criterion,
.
Solving,
.
When comparing to the equation in Zero Forcing equalizer, apart from the term both the equations are comparable. Infact, when the noise term is zero, the MMSE equalizer reduces to Zero Forcing equalizer.
Simulation Model
The Matlab/Octave script performs the following
(a) Generate random binary sequence of +1’s and -1’s.
(b) Group them into pair of two symbols and send two symbols in one time slot
(c) Multiply the symbols with the channel and then add white Gaussian noise.
(d) Equalize the received symbols
(e) Perform hard decision decoding and count the bit errors
(f) Repeat for multiple values of and plot the simulation and theoretical results.
Click here to download Matlab/Octave script for simulating BER in a 2×2 MIMO channel with MMSE equalization for BPSK in Rayleigh channel
Figure: BER plot for 2×2 MIMO with MMSE equalization for BPSK in Rayleigh channel
Summary
Compared to the Zero Forcing equalizer case, at BER point, it can be seen that the Minimum Mean Square Error (MMSE) equalizer results in around 3dB of improvement.
References
[DIG-COMM-BARRY-LEE-MESSERSCHMITT] Digital Communication: Third Edition, by John R. Barry, Edward A. Lee, David G. Messerschmitt
[WIRELESS-TSE, VISWANATH]
Related posts
- MIMO with MMSE SIC and optimal ordering
- MIMO with Zero Forcing Successive Interference Cancellation equalizer
- MIMO with ML equalization
- MIMO with Zero Forcing equalizer
- Six equalizers for V-BLAST
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Hi & Thanks for your good posts
will you combine the concepts that you have investigated so far step by step and show how is the performance of
MIMO-OFDM systems with alamouti coding and different equalizers such as MMSE in multi-tap Rayligh channel as your conclusion and complex of all of these concepts.
Thanks Krishna alots! Try to research MIMO, I also have a project with MIMO system: Using Genetic Alogrithms as a tool to handle large numbers of users. I hope we can discuss this project if you don’t mind.
Regards!
@mehrkhan: With Alamouti coding, since the channel H^H*H is a diagonal, I do not think there is advantage by using MMSE equalization. Anyhow will check and let know.
@nano686: Were you referring to large MIMO system where the channel dimension is large such that the simple matrix inversion approaches becomes impractical?
Sure, we can discuss. Please email the details.
sir
do you know how to find channel coefficients using expectation and maximization algorithm for mimo cdma model.if so please explain through matlab
@phaneendra: sorry, am not familiar.
in addition to my first request for investigating the MIMO-OFDM system one question is that how can we maintain the channel coefficients equal for two consecutive symbol(for satisfying the Alamouti decoding scheme) while we are modulating symbols on for example 64 subcarriers of one antenna and the channel has 3-taps?
@mehrkhan: The presence of a 3-tap channel does not imply that channel varies with time – it just means that channel varies across frequency.
There can be channel conditions (for eg indoor), where it can be reasonably assumed that the channel remains constant over two symbol periods.
Dear Krishna Pillai,
Thanks for the very interesting blog ..
I am wondering whether it makes any sense to apply MMSE and ML equalization on SISO communication systems in order to get over the ZF equalization major problem of noise enhancement (when channel is in deep fade).
In such a case, how is the theoretical BER performance of BPSK (for instance) over flat rayleigh (given by proakis, Digital communications) affected ? can u reccommend any papers that calculate the theoretical BER performance of ML and MMSE equalization over flat fading .. Most papers i got assume ZF equalization when it comes to SISO systems !
@Shefo666: I think the ZF equalizer is optimal in SISO systems.
With a simple Matlab/Octave script may I try to show that ZF and MMSE equalizer gives the same BER. Infact, am not sure whether we can call it as ZF/MMSE equalization – as there is no interference terms.
% Matlab/Octave code snippet for comparing zero forcing and MMSE equalization for SISI
clear
N = 10^4 % number of bits or symbols
ip = rand(1,N)>0.5; % generating 0,1 with equal probability
s = 2*ip-1; % BPSK modulation 0 -> -1;
Eb_N0_dB = 3;
n = 1/sqrt(2)*[randn(1,N) + j*randn(1,N)]; % white gaussian noise, 0dB variance
h = 1/sqrt(2)*[randn(1,N) + j*randn(1,N)]; % Rayleigh channel
% Channel and noise Noise addition
y = h.*s + 10^(-Eb_N0_dB/20)*n;
% equalization
yHat_zf = y./h; % zf equalization
yHat_mmse = 1./(h.*conj(h)+ 10^(-Eb_N0_dB/10)).*conj(h).*y; % mmse equalization
% receiver – hard decision decoding
ipHat_zf = real(yHat_zf)>0;
ipHat_mmse = real(yHat_mmse)>0;
% counting the errors
nErr_zf = size(find([ip- ipHat_zf]),2)
nErr_mmse = size(find([ip- ipHat_mmse]),2)
Can see that nErr_zf and nErr_mmse is the same. Hope this helps.
Yeah its very helpful .. thanks for the clarification
@Shefo666: And for completeness, I thought of adding ML decoding also into the code snippet. You can see that the error in SISO Rayleigh channel for ZF, MMSE, ML are the same.
% ml decoding
ipHat_ml = abs(y – h.*1) < abs(y+h.*1);
% counting the errors
nErr_ml = size(find([ip- ipHat_ml]),2)
Yeah they provide the same performance .. ZF is indeed optimal when it comes to SISO systems
hello krishna
i want to simulate a MC-CDMA system that in it calculate PAPR and detect diffrent users with BER calculating and can not do this completly. for this i ask you that if have no problem for you help and give me a matlab file about this problem
thanks a lot for your attention
@hossein: Sorry, I do not have a Matlab code which explicitly does this. For PAPR, you may refer to the posts
with the URI: http://www.dsplog.com/tag/papr/
Since MC-CDMA can be built upon OFDM simulations, you may refer to the posts with the URI: http://www.dsplog.com/tag/ofdm/
Hope this helps.
this is the case of MIMO with MMSE equalizer.but if we want to use it in the OFDM and “not” in MIMO ofdm, then what would the matlab code be?can you kindly help me on this?
@shahid: The same concepts hold good. However, for the SISO OFDM case, I do not see the need for using a MMSE equalizer. A simple ZF equalizer should suffice. I have written a post on BPSK over OFDM in a Rayleigh multipath channel
URI: http://www.dsplog.com/2008/08/26/ofdm-rayleigh-channel-ber-bpsk/
Hope this helps.
Hi Kirshna
Can u please give some stuff on MMSE equalization or can u explain the method u used for equalization.
yHat_mmse = 1./(h.*conj(h)+ 10^(-Eb_N0_dB/10)).*conj(h).*y; % mmse equalization
Thanks
@Abrar: When equalizing by H^H matrix, we are considering the noise variance too. If the noise is high, the noise will be the dominating component.
hai sir can i get the codings related soft parallel interference cancellation algorithm (SPIC) for MIMO OFDM for the Spatial Multiplexing Interference(SMI)
@Raaghavan: I have written posts on successive interference cancellation
URI: http://www.dsplog.com/tag/sic/
Hope this helps.
Thank you sir
What is the “NoI” means and why it is equal to 10^(-snr_dB(i)/10)
thanks alot
@John: N0 is the noise variance and its multiplied by an identity matrix I (to ensure that matrix algebra holds good). Since N0 is the noise variance, we define it as 10^(-Eb_N0_dB(ii)/10)
Hope this helps.
hi? sir,
when i simulated MMSE 16QAM 2×2 sysem
it’s performance almost same as zero-forcing
is it a right? i used below equation
inv(H^H*H+10^(-SNR/10)*Im)*H^H*R;
(Im:identity matrix, H:channel, R:received
signal inv:inverse)
is it depend on noise variance?
please answer me thanks
@dokich: From the analysis done on BPSK modulation, the MMSE equalier should provide slightly better performance that ZF equalizer (for any value of noise variance). So, there might be some bug in your simulation setup.
Try forcing the noise variance term in the MMSE equalizer to be zero. Then you should get the identical performance to ZF equalizer. Once you obtain that, you may try with different values of noise variance.
Hope this helps.
thank you for your answer,
i try to your advice but it still worse than ZF equalizer.
this is my code
H = (randn(N,M) +sqrt(-1) * randn(N,M)) / sqrt(2)
sigma2 = 0.5 / (10^(SNR/10))
noise = sqrt(sigma2) * (randn(N, 1) + sqrt(-1) * randn(N, 1))
for(a=1:no_data)
u=[]; %Tx data
for s = 1:M
u=[u;data(s,a)];
end
m=M;
n=N;
m=2*m;
n=2*n;
r=H*u+noise(:,a);
rmm = [real(r);imag(r)];
umm = [real(u);imag(u)];
Hmm = [[real(H),-imag(H)];[imag(H),real(H)]];
Im=eye(m,m);
ummHat=inv(Hmm’*Hmm+10^(-SNR/10)*Im)*Hmm’*rmm;
[row col]=size(ummHat);
for(j=1:row)
for(k=1:col)
if ummHat(j,k)<-2 ummHat(j,k)=-3;
elseif ummHat(j,k)<0 ummHat(j,k)=-1;
elseif ummHat(j,k)<2 ummHat(j,k)=1;
else ummHat(j,k)=3;
end
end
end
if sum(norm(ummHat-umm)^2)~=0
error_MMSE=error_MMSE+1;
end
end%end of no_data MMSE
if you have a time please help me to find problem sorry to border you thanks
@dokich: From a quick look, your equalizer equation seems correct. Do not know whether there are hidden bugs.
Did you want to try with modifying the Matlab code which I have provided in the post.
Hola Krishna,
gracias por este blog, estoy interesada en procesado de señal para MIMO. Hasta ahora yo me he centrado en banda estrecha utilizando vblast, qostbc… pero quiero entrar en banda ancha y no se que algoritmo utilizar para evitar la ISI. Tienes algún algoritmo implementado en Matlab que me pueda servir de base para yo empezar en banda ancha?
Muchas gracias.
@MIMO: Can you please translate your query to english
OK thank you very much
Hi mr.khrisna can you help me about mmse detector on cdma receiver?? it’s same matlab script with MMSE Equalizer??
thanks
@jaka: For CDMA, the concept remains the same. However, I would think that to simulate the CDMA MMSE detector case, the Matlab script in this post need to be modified to include:
(a) spreading and depreading,
(b) make flat fading rayleigh channel to a frequency selective channel
(c) remove mimo and make it a single spatial stream.
oke, Mr. thanks for you advice.. nice to know you..
Hi Krishna,
I wanted to know how the equalization parameter look like if Eqz is done in the frequency domain for MMSE. (the scenario thought of here is for equalizing after DFT in an OFDM chain).
TIA,
Best Regards,
Gavigod
@gavigod: Hmm… even for OFDM, the equalzation parameters will look similiar (as long as the channel is assumed to be flat fading – which is reasonable in OFDM).
Do you agree?
yes. it is, thanks krishna.
hai sir,
please send QOSTBC for MIMO channels based on freqency flat rayleigh fading channels…
@Murali: I have not worked on QOSTBC
For BPSK modulation in flat Rayleigh channel, you may look up @
http://www.dsplog.com/2008/08/10/ber-bpsk-rayleigh-channel/
For QPSK in AWGN, you may look up @
http://www.dsplog.com/2007/11/06/symbol-error-rate-for-4-qam/
Hope this helps.
hai sir ,
plz send the COSTBC matlab code for MIMO channels
Perhaps the title could be spesify because there are several MIMO mode (STBC/STC, BLAST-family,SM, STTC).
If I’m not mistaken, MIMO that your refer in this article is SM Mode (usually using V-Blast).
So, I think the title sould be “Spatial Mutliplex MIMO…” or “V-BLAST MIMO…”
thx
@andjas: Yes, the post describes spatial multiplexing (aka V-BLAST). I would prefer keeping the title as I think V-BLAST is the simplest form of MIMO (atleast to explain) when compared to STBC etc.
Ok Mr. Krishna, that’s your right because what you said is true.
Mr. Krishna, I’ve been making correlated and uncorrelated MIMO channel model based on WiMAX Forum Channel Model Recommendation and ITU Channel Model recommendation. As I know, channel model doesn’t have parameters that can be directly analyst, is our channel model correct or not.
1. Could you give me some advice/ material about steps/process to make MIMO Channel?
2. Could you explain how to analyst correctness of my MIMO channel model?
3. Could you send me example/script of MIMO channel model (correlated/ uncorrelated)? So, I can compare my model with yours.
Thank you, A.W.
@andjas: My replies:
1. One great reference is the channel model defined by the High Throughput study group for 802.11n standards development.
Tgn Channel Models, Vinko Erceg et al. The document provides a good overview of MIMO channel modeling – includng the effect of antenna correlation (based on antenna spacing). effect of fluorescent lights, doppler for different indoor multipath characteristics.
(2). (3). The Matlab source code for TGN channel models is available in public domain. You may check @
L. Schumacher “WLAN MIMO Channel Matlab program,” download information: http://www.info.fundp.ac.be/~lsc/Research/IEEE_80211_HTSG_CMSC/distribution_terms.html
I think, those models should be a good point to start.
Most of my models are simple flat fading un-correlated rayleigh channel. Hence, might not be of much help to you.
Hope this helps.
Thanks a lot Mr.Krishna.
Em dang can tim file mo phong he thong MIMO-OFDM bang matlab de thay ro tac dung cua phan tap duoc ung dung trong he thong nay.
@quynhchi: Can you please translate that query into english.
cher monsieur
J’essaye de simuler un système SC-FDMA qui est proche du OFDM.
Donc j’ai transmet mes symboles sur un canal de Rayleigh que j’ai modalisé par la fonction suivante:
T(i)=sqrt(2).*(xtr(i)*(t(i)-ret(i))).*exp(2*pi*j*(fc+wn(i))*t(i)).*exp(-j*2*pi*fc*d/c);
Avec T(i) les symboles recus et xtr(i) les symboles émis et ret(i) et le retard de propagation.
Ensuite j’ai ajouté un bruit gaussien:
Eb=G*G’;
RSB=10;
N0=Eb/(10^(RSB/10));
b=sqrt(N0/2)*randn(1,length(T));
je recoit alors
W=T+b;
le problème maintenant que lorsque je veux égalisé avec un mmse..c’est que je suis incapable de déduire la fonction de transfert du canal h…aidez moi s’il vous plait!!
thinks
cher monsieur
J’essaye de simuler un système SC-FDMA qui est proche du OFDM.
Donc j’ai transmet mes symboles sur un canal de Rayleigh que j’ai modalisé par la fonction suivante:
T(i)=sqrt(2).*(xtr(i)*(t(i)-ret(i))).*exp(2*pi*j*(fc+wn(i))*t(i)).*exp(-j*2*pi*fc*d/c);
Avec T(i) les symboles recus et xtr(i) les symboles émis et ret(i) et le retard de propagation.
Ensuite j’ai ajouté un bruit gaussien:
Eb=G*G’;
RSB=10;
N0=Eb/(10^(RSB/10));
b=sqrt(N0/2)*randn(1,length(T));
je recoit alors
W=T+b;
le problème maintenant que lorsque je veux égalisé avec un mmse..c’est que je suis incapable de déduire la fonction de transfert du canal h…aidez moi s’il vous plait!!
thinks
Hi Mr. Krishna,
Do you think that the performance would change in a 4QAM scheme? I have simulated and it is slightly worse but it does not make sense to me…
thanks
@neon: I think, symbol error rate performance with 2×2 4-QAM should be slightly better than symbol error rate performance of 1×1 4 QAM case.
Agree?
Hi,
I agree that it should be slightly better than 1×1 case. What I meant was the comparison between bpsk case and 4 qam case. for zero forcing and 2×2 mimo system both bpsk and 4qam achieve the same performance, but for the mmse case 4qam is slightly worse than bpsk, i suppose it comes from the fact that 4qam is more affected by the noise. Am I right?
thanks Mr. Krishna
@neon: What was your comparison metric – bit error rate or symbol error rate? Were you using Eb/No or Es/No?
Hi,
I was using Eb/No.
Thanks
Hi
I was using Eb/No and BER,
Thanks
sir pls can any one send me the matlab code for space frequency coded bs-cdme for broadband mobile comm(2X2 MIMO)
@pradhyu: Sorry, I have not worked on space frequency coding.
I am working on developing IQ Imabalance compensation scheme in MIMO-OFDM systems. I have done IQ Compensation in OFDM systems but when I am combining MIMO with OFDM, I am not getting proper BER curve and the results r too bad, I have used rayleigh channel and added AWGN noise to the signal transmitted and assumed that the channel is flat, known and the path gains doesnt change for two OFDM symbol durations.I have used Alamouti STC for 2X2 MIMO channel. Please help me getting a solution and send me some related MATLAB codes
@NAVAL: It’s bit hard to say, why the code which is working for 1×1 OFDM is not working for 2×2 OFDM. I think to nail the problem, you should remove awgn, channel etc and make sure that your algorithm is working in ideal conditions. Btw, it sounds like you are trying to do receive I/Q imbalance estimation/compensation? Why do you need the ‘channel is constant for two symbol duration’ assumption.
Dear sir,
I have utilized the MMSE equalizer defined by equation 1./(h.*conj(h)+ 10^(-Eb_N0_dB/10)).*conj(h) for frequency domain equalization of a single carrier modulation system. Now i have to give the reference of this equation in my research work. So kindly tell me the book or any paper which includes so that i could refer this. Plz also provide a soft copy (pdf, web link) if available.
hello,
i’m see your script of MIMO equalizer MMSE for 2×2 and my doubt is, why don’t you multiply for 2 the noise? because you consider Es = 1 (ok) but there is 2 antennas. It would want so (10^(-Eb_N0_dB(ii)/20)*2), not?
@mimo: Well, recall that we have two receive chains, and the term 10^(-Eb_N0_dB(ii)/20) is applied for both.
Makes sense?
Why don’t use the same channel (h) for a Eb_N0? I use the following code, Is it well, not??
clear
N = 60000; % number of bits or symbols
Eb_N0_dB = [0:20]; % multiple Eb/N0 values
nTx = 2;
nRx = 2;
for ii = 1:length(Eb_N0_dB)
N0 = 10.^(-Eb_N0_dB./10);
criterio =’mmse’;
esquema = ‘psk’;
M = 2;
fuente = load(’simTx_120000_2′);
fuenteselec = fuente.x;
fuentedecimal(1,:) = fuenteselec(1,1:60000); % transmito según el estudio de convergencia 60000 símbolos para un error del 1%
bitsTx = length(fuentedecimal) * log2(M);
fuentebinario = decimal_bin (fuentedecimal,M);
fuenteDemux = demux(fuentedecimal,nTx);
%Modulo los datos a transmitir (cada antena tx 1024 símbolos).
sMod = modulador(fuenteDemux,esquema,M,1);
H = 1/sqrt(2)*[randn(nRx,nTx) + j*randn(nRx,nTx)]; % Rayleigh channel
n = sqrt(N0(ii)/2)*(randn(nRx,N/nTx) + j*randn(nRx,N/nTx)); % white gaussian noise, 0dB variance
% Channel and noise Noise addition
y = H*sMod + n; % Dividimos por 20 porque así nos ahorramos hacer la raiz.
% Receiver
% Forming the MMSE equalization matrix W = inv(H^H*H+sigma^2*I)*H^H
% H^H*H is of dimension [nTx x nTx]. In this case [2 x 2]
% Inverse of a [2x2] matrix [a b; c d] = 1/(ad-bc)[d -b;-c a]
G = inv(H’*H + N0(ii)*eye(nTx))*H’;
yMod = G*y; % H^H * y
[Y,Es_rx] = demodulador(yMod,esquema,M);
datosmux = mux(Y);
Datosbinarios = decimal_bin (datosmux,M);
if length(fuentebinario) ~= length(Datosbinarios)
Datosbinarios = Datosbinarios(1,1:length(fuentebinario));
end
nErr(ii) = sum(fuentebinario~=Datosbinarios);
% sMod = reshape(yMod,[1,N]);
%
% % receiver – hard decision decoding
% ipHat = real(sMod)>0;
%
% % counting the errors
% nErr(ii) = size(find([ip- ipHat]),2);
end
simBer = nErr/N; % simulated ber
EbN0Lin = 10.^(Eb_N0_dB/10);
theoryBer_nRx1 = 0.5.*(1-1*(1+1./EbN0Lin).^(-0.5));
p = 1/2 – 1/2*(1+1./EbN0Lin).^(-1/2);
theoryBerMRC_nRx2 = p.^2.*(1+2*(1-p));
close all
figure
semilogy(Eb_N0_dB,theoryBer_nRx1,’bp-’,'LineWidth’,2);
hold on
semilogy(Eb_N0_dB,theoryBerMRC_nRx2,’kd-’,'LineWidth’,2);
semilogy(Eb_N0_dB,simBer,’mo-’,'LineWidth’,2);
axis([0 25 10^-5 0.5])
grid on
legend(’theory (nTx=2,nRx=2, ZF)’, ‘theory (nTx=1,nRx=2, MRC)’, ’sim (nTx=2, nRx=2, MMSE)’);
xlabel(’Average Eb/No,dB’);
ylabel(’Bit Error Rate’);
title(’BER for BPSK modulation with 2×2 MIMO and MMSE equalizer (Rayleigh channel)’);
@MIMO: Well, I did not understand your question.
Sir,
Can you just give me your email address, so that I can mail you my queries. I am working on MIMO OFDM equalization techniques and have a series of questions that I have doubts in. Will you please kindly send your address at cvvarun_raj@yahoo.co.in?
Thanking You
Regards
Varun
@ Varun Raj: you may find my email address in the page
http://www.dsplog.com/contact-us/
Further, you may find many articles discussion receiver structures for MIMO @ http://www.dsplog.com/tag/mimo.
very good
Thanks Krishna alots! I have question about your programme. When you compute the inverse of (conj(H)*H) matrix you use:
hCof(1,1,:) = sum(h(:,2,:).*conj(h(:,2,:)),1);
hCof(2,2,:) = sum(h(:,1,:).*conj(h(:,1,:)),1);
hCof(2,1,:) = -sum(h(:,2,:).*conj(h(:,1,:)),1);
hCof(1,2,:) = -sum(h(:,1,:).*conj(h(:,2,:)),1);
whereas the product between two matrices is:
hCof(1,1,:) = sum(h(2,:,:).*conj(h(:,2,:)),1);
hCof(2,2,:) = sum(h(1,:,:).*conj(h(:,1,:)),1);
hCof(2,1,:) = -sum(h(2,:,:).*conj(h(:,1,:)),1);
hCof(1,2,:) = -sum(h(1,:,:).*conj(h(:,2,:)),1);
when I apply these changes I do not find the same results.
best regards
@Wassim: Note that am computing the cofactor of the matrix (H^H*H). Inverse of a [2 x 2] matrix
[a b; c d] = 1/(ad-bc)[d -b;-c a]
The code which you have pasted does not include the matrix rearrangement to compute the cofactor. Agree?
How we can calculate bit error rate.
@Alvina: Well, count the number of differences between received bits and transmitted bits and divide that by total number of transmitted bits …
Hello,
how do you actually implement mmse for a 2 tx 1 rx system?having a bit of trouble undestanding it for these settings
@safwan: But, why would you want an MMSE for a 2 transmit, 1 receive system?
im actuallt trying to do channel estimation using mmse for 2 tx 1 rx but having trouble understanding it…please help
@safwan: Well, I do not quite understand the need for having an MMSE equalizer for a 2 transmit 1 receive system. If there is no coding at the transmitter, then 2 transmit 1 receive system performs as if its a 1 transmit 1 receive system. Agree?
well,im actually implementing MIMO-OFDM on the 2 tx and 1 rx…i want to get the most optimum training sequence and then estimate the channel using mmse
@safwan: For chnnel training, you may use ideas suggested in 802.11n spec. Transmitting from both the antenna with a multiplication by an orthogonal matrix. You can have a look at the old version of 802.11n spec @
https://mentor.ieee.org/802.11/dcn/05/11-05-1102-03-000n-joint-proposal-phy-specification.doc
Or you could use CAZAC sequences with cyclic shift between MIMO users as it is in LTE uplink.
hey sir,can u plzz telme dat how we can find theoretical ber for mimo systems???
and also can u guide me dat can we send a copy of data from 2 transmitters at da same tym instead of selecting a pair of data n den sending it,,is it a right approach???
@maya: Well, from the simulations which I did a 2 transmit 2 receive MIMO V-BLAST system with Zero Forcing Equalizer in flat fading rayleigh channel, performed about identically with a 1 transmit 1 receive system.
Well, if we send the same information from two transmit antennas, I believe there is not much performance gain in flat fading rayleigh channel. I have written a brief post on transmit beamforming, which briefly touches upon this topic
http://www.dsplog.com/2009/04/13/transmit-beamforming/
Hope this helps.
thnx for ur response sir..i think em confusing everything
i have juz strted doen work on it,can u plzz guide me what is da difference between diversity and beamforming???
@maya: Well, I diversity in the general sense means – the using the extra information which is available and/or transmitted to improve the reliability of the communication link. In general, if we have multiple antennas at the receiver, we have to think of intelligent ways to do receive diversity. Similarly, if we have multiple transmit antennas, we have to figure out ways of processing of the data such that the reliability of the link is increased.
Beamforming is one way of doing transmit diversity, where the knowledge of the channel is used to process the information at the transmitter.
Hope this helps.
I am studying about MIMO Tomlinson Harashima Precoding. Do you have any document or program relate to MIMO THP?
@dungpt: Sorry, am not faimiliar with MIMO Tomlinson Harashima Precoding
DEAR sir…i m pragya and i hav heard about u a lot.
i m in final yr so i hav 2 mak a projct and my projct is MIMO WID MMSE EQUALISER..
so if possible plzz guide me as i hav no knowledge regarding dis..
plzzzzzzzzzzzzzzz
my ID z pragyamitra@gmail.com
i am waitin 4 ur respnse desperatly…
@pragya: I hope this post serves as a good introduction. If you have additional queries, plz revert.
Dear Krishna Pillai:
I want to combine V-BLAST and OFDM, how can I simulate this in Matlab, I know that you have not done that but can you guide me or support me for archive this goal.
On advance, thanks a lot
@Mijares: Sure, you may ask queries.
Thanks for your help, in this moment I am trying to combine Alamouti with OFDM but the BER that I got is very high, this is the code:
clear all
nFFT = 10
nDSC = 2
nBitPerSym = 2
nSym = 2
N = 4
EbN0dB = [0:35];
EsN0dB = EbN0dB + 10*log10(nDSC/nFFT) + 10*log10(10/14);
for ii = 1:length(EbN0dB)
ip = rand(1,N)>0.5
s = 2*ip-1
sCode = zeros(2,N)
sCode(:,1:2:end) = (1/sqrt(2))*reshape(s,2,N/2)
sCode(:,2:2:end) = (1/sqrt(2))*(kron(ones(1,N/2),[-1;1]).*flipud(reshape(conj(s),2,N/2)))
xF = [zeros(nSym,4) sCode(:,[1:nBitPerSym/2]) zeros(nSym,1) sCode(:,[nBitPerSym/2+1:nBitPerSym]) zeros(nSym,3)]
xt = (nFFT/sqrt(nDSC))*ifft(fftshift(xF.’)).’
xt = [xt(:,[7:10]) xt]
nTap = 10
ht = 1/sqrt(2)*1/sqrt(nTap)*(randn(nSym,nTap) + j*randn(nSym,nTap))
hF = fftshift(fft(ht,10,2))
for jj = 1:nSym
xht(jj,:) = conv(ht(jj,:),xt(jj,:))
end
xt = xht
xt = reshape(xt.’,1,nSym*(14+nTap-1))
nt = 1/sqrt(2)*[randn(1,nSym*(14+nTap-1)) + j*randn(1,nSym*(14+nTap-1))]
yt = sqrt(14/10)*xt + 10^(-EsN0dB(ii)/20)*nt;
yt = reshape(yt.’,14+nTap-1,nSym).’
yt = yt(:,[5:14])
yF = (sqrt(nDSC)/nFFT)*fftshift(fft(yt.’)).’
yF = yF./hF
yMod = yF(:,[3+[1:nBitPerSym/2] 4+[nBitPerSym/2+1:nBitPerSym] ])
ipModHat = 2*floor(real(yMod/2)) + 1
ipModHat(find(ipModHat>1)) = +1
ipModHat(find(ipModHat<-1)) = -1
ipBitHat = (ipModHat+1)/2
ipBitHat = reshape(ipBitHat.',nBitPerSym*nSym,1).'
nErr(ii) = size(find(ipBitHat – ip),2)
end
simBer = nErr/(nSym*nBitPerSym)
I hope you can help me to increase the performance of the Ber.
Regards
thanks a lot for your help, first I want to combine Alamouti with OFDM, but the problem is that I get a very high BER, this is the code:
clear all
nFFT = 10
nDSC = 2
nBitPerSym = 2
nSym = 2
N = 4
EbN0dB = [0:35];
EsN0dB = EbN0dB + 10*log10(nDSC/nFFT) + 10*log10(10/14);
for ii = 1:length(EbN0dB)
ip = rand(1,N)>0.5
s = 2*ip-1
sCode = zeros(2,N)
sCode(:,1:2:end) = (1/sqrt(2))*reshape(s,2,N/2)
sCode(:,2:2:end) = (1/sqrt(2))*(kron(ones(1,N/2),[-1;1]).*flipud(reshape(conj(s),2,N/2)))
xF = [zeros(nSym,4) sCode(:,[1:nBitPerSym/2]) zeros(nSym,1) sCode(:,[nBitPerSym/2+1:nBitPerSym]) zeros(nSym,3)]
xt = (nFFT/sqrt(nDSC))*ifft(fftshift(xF.’)).’
xt = [xt(:,[7:10]) xt]
nTap = 10
ht = 1/sqrt(2)*1/sqrt(nTap)*(randn(nSym,nTap) + j*randn(nSym,nTap))
hF = fftshift(fft(ht,10,2))
for jj = 1:nSym
xht(jj,:) = conv(ht(jj,:),xt(jj,:))
end
xt = xht
xt = reshape(xt.’,1,nSym*(14+nTap-1))
nt = 1/sqrt(2)*[randn(1,nSym*(14+nTap-1)) + j*randn(1,nSym*(14+nTap-1))]
yt = sqrt(14/10)*xt + 10^(-EsN0dB(ii)/20)*nt;
yt = reshape(yt.’,14+nTap-1,nSym).’
yt = yt(:,[5:14])
yF = (sqrt(nDSC)/nFFT)*fftshift(fft(yt.’)).’
yF = yF./hF
yMod = yF(:,[3+[1:nBitPerSym/2] 4+[nBitPerSym/2+1:nBitPerSym] ])
ipModHat = 2*floor(real(yMod/2)) + 1
ipModHat(find(ipModHat>1)) = +1
ipModHat(find(ipModHat<-1)) = -1
ipBitHat = (ipModHat+1)/2
ipBitHat = reshape(ipBitHat.',nBitPerSym*nSym,1).'
nErr(ii) = size(find(ipBitHat – ip),2)
end
simBer = nErr/(nSym*nBitPerSym)
I hope you can help me for increase the performance of the BER
Thanks for all
Regards
Hello,
Have you received help??
Thanks.
@ Mijares: Sorry, due to time constraints, I typically do not debug the code. If you have explicit queries, I can try to answer.
what is mmse? and why we use it instead of other equaliser?
@pragya: MMSE – Minimum Mean Square Error. As you can seen from the BER curves, the BER with MMSE equalizer is lower than BER with Zero Forcing (ZF) equalizer.
Hola de parte de parejaspareja.es, encontre tu blog navegando por la red buscando banda ancha en google. Me parece super interesante la información que tienes en tu blog y sin lugar a dudas regresare a leerlo. Tengo una pregunta, si podria traducir tu blog “MIMO with MMSE equalizer” y añadirlos a un de mis blogs en italiano? Y por supuesto con el link direccionando a tu blog. Estare esperando tu respuesta. parejaspareja.es
Hi Krishna,
I understand that MMSE is minimizing the equation
http://www.dsplog.com/cgi-bin/mimetex.cgi?E\left\{%20\mathbf{\left[Wy-x\right]\left[Wy-x\right]}^H\right\}
where can I find, what’s the reason for this? why we choose that equation. (I don’t even know such basics). can you suggest a book.
and in the above comment Parejas is asking you to reuse your post in his italian site(If my translation is correct). You gave him permission ?
@WirelessBewbie: The derivation of the MMSE equalizer is provided in Chapter 10.3.3. of Digital Communication: Third Edition, by John R. Barry, Edward A. Lee, David G. Messerschmitt. Need to write an article about this. Another addition to the to-do list.
Btw, I have not given permission to Parejas. It seems like a spam comment.
Thanks for the previous reply
In the sample code given, the noise variance is n, but it is not used in the receiver, instead of that 10^(-Eb_N0_dB(ii)/10) is used.
Is it because the n is not known to receiver? So we have to measure the SNR in the receiver in practical case?
Thanks in advance
@WirelessNewbie: In the simulation code, n is the noise voltage signal. For MMSE equalizer, we do not need the noise voltage, rather we only need the variance of the noise. Hence the term 10^(-Eb_N0_dB(ii)/10) is used. Agree?
Yes, a practical MMSE implementation needs to know the measure the SNR at the receiver.
can you plz help me out to write Matlab codes about one-ring model, two-ring model, iid model, kronecker model in mimo
thanks in advance
@Hamad: Sorry, am not familiar with the one/two ring models which you are proposing.
Hi
Can we user MMSE in case of SISO/SIMO?
@WirelessNewbie: Well, in the case of SISO, as there is no interference, the Zero Forcing equalization is optimal.
If SIMO case, we can use the Maximal Ratio Combining. http://www.dsplog.com/2008/09/28/maximal-ratio-combining/
Hi Krishna
Thanks for spending your valuable time on it
could you please refer :
http://books.google.com/books?id=3DY6OAIGu0kC&printsec=frontcover&source=gbs_v2_summary_r&cad=0#v=onepage&q=&f=false
(Introduction to Space Time Communications)
Which gives a comparison of ZF, MMSE,etc for SISO
It shows MMSE is better than ZF for SISO.
Regards
@WirelessNewbie: Sorry, the page 141 is not available from the link you provided. Let me try to get the book from the library, and I will respond.
One query: Is the claim that “ZF is better than MMSE for SISO” for a flat fading channel?
can we use MMSE(apart from MRC) for SIMO?
@Chiru: But, in single input mutliple output, there is no interference term and hence I guess MRC is optimal. Agree?
Thanks for the previous reply
But, if i Use MMSE it will reduce error(Mean Square) but MRC cant reduce it…….So, how can we decide which one is suitable ………
@Chiru: MMSE reduces the error due to interference. However, if its a SIMO case, then there is no interference. Hence MRC is optimal. Agree?
a) Wont MRC have noise enhancement problem? (Because I still dont see clear difference between ZF and MRC)
b) Even if it is SIMO, in real system you will always have interference. So, my answer would be MMSE (with IRC option, where we dont estimatejust noise variance but rather noise+interferece covariance matrix).
How Can i Calculate Noise Variance based on Received Reference and Transmitted Reference(Pilots) Symbols. Which i want to use it in MMSE Equation
Thanks & Regards,
Venki
@Venki: I am just guessing, if we know that
y = x + n, where
y is the received pilots,
x is the transmitted reference pilots,
n is the noise.
Then the variance of n can be estimated by finding E { (y-x)^2 }, where E{} is the expectation operator.
Agree?
Thanks for the previous reply………………..
If i have Reference symbols in frequency domain then can i add N0=(y-x)^2 this directly for Noise Variance Calculation……..bcz i thought noise addition should be in Time Domain……….so do i need to perform IFFT(N0) for Time domain conversion?????????????
@Venki: Noise addition in time domain. But, I think the variance does not change even if we compute in frequency domain or in time domain.
@Venki: You can do an ifft(N0) to find the noise in frequency domain. But, the variance of the noise term N0 does not change irrespective of whether we do ifft() or not. Hence doing ifft() is not needed. Agree?
Thats correct, we do not need to be in time domain. I think your solution is ok only for AWGN because otherwise you dont have channel information involved.
Another solution:
y1 <- first received reference symbol
y2 <- second received reference symbol
Then the variance of n can be estimated by finding E { (y1-y2)^2 }, where E{} is the expectation operator.
This approach is ok as long as there is not Timing offset is involed, otherwise you pilots will have phase rotation and as a results biased noise estimate.
Hi , how can we extend the above MIMO implementation to a general ‘n’ transmitters and ‘m’ receivers case ?
Is it possible to code for a general case or should we have separate implementations for each case (like 2×3, 3×2, 3×3, and so on..)
Also, say we have 4 transmitters and 4 receivers, how does the MMSE Equalization work in this case ?
Thanks !
@Kartik: Extending the Matlab code to an nRx x nTx case involves modification to the equalizer. In the current code, we have a 2×2 matrix inversion. If we go for higher dimension matrices, we need to change the logic for matrix inversion accordingly. Alternatively, one can use the pinv() function – but then, we loose some of the vectorizing advantages (which results in faster execution) which we now have in the code.
This is one of the best discussion forum That I have come across. Keep it up Guys. Thanks to Krishna Pillai Garu.
@Krishna: Thanks.
Hi Krishna Pillai
I think your result of the weight coefficient is not correct. could you show how did you get this result formula?
Thanks
@Shengyan: This equation is discussed in most text books. You may refer Chapter 10.3 in Digital Communication: Third Edition, by John R. Barry, Edward A. Lee, David G. Messerschmitt
Hi Krishna Pillai
With your model the dimension of the noise correlation matrix shall be Nrx xNrx. But the dimension of the noise correlation matrix in the equalizer is Ntx x Ntx. What is the meaning of this and how can you get it in case of Nrx>Ntx?
Thanks.
@Shengyan: The noise is of dimension [Nrx x 1]. What is the noise correlation matrix which you are refering to , is it (H^H*H) ?
Hi,
How is the MMSE equalization done in the case of a frequency selective channel ?
(As it is not a simple multiplication, but convolution instead)
Thanks..
@KB: Yeah, the equations change. I preparing articles for the multipath channel case.
Hi,
Its a great post indeed.
I am having a very basic doubt. I am just stating the flow for my understanding:
1) Signal ‘X’ is transmitted with pilots.
2) Received signal Y = H.X + N
3) At receiver channel estimation is done i.e ‘H’ is calculated with the help ref. signals/pilots, received).
4) Then ‘W’ is calculated.
5) Now I need to retrieve the original signal (X). Can you please tell me how to do that in any case, zero forcing or MMSE. Is it just linear division or something else is involved. It will be helpful if you may tell the equation as well.
@Gaurav: Your steps are right.
In ZF, to estimate the original signal X, we need to find a matrix W which makes W*H = I.
This is a matrix division operation.
Hi,
I have a doubt:
if : W=[H'H + NoI]^-1 * H’
and if sigma_2=No/2
why do you add 10^(-Eb_N0_dB(ii)/10) constructing the hCof instead of 2* 10^(-Eb_N0_dB(ii)/10) ??? I mean, 10^(-Eb_N0_dB(ii)/10) is No/2 and in W it seems right to put No, so 2* 10^(-Eb_N0_dB(ii)/10)
The BER you can obtain with this “2*” is slightly different but my interest is mainly to correctly understand what SHOULD be there, No or No/2…
Please answer. Might be helpful
Thanks in advance
Francesco
Please answer
@Francesco: Good catch. Infact, when I coded I did not really think about it. Which plot showed better performance, with 2 or without?
Hi,
seems that we get the best performance (lower BER @ same SNR) without the 2, even if the difference is very small. Anyway this doesn’t mean that the “2″ shouldn’t be there, it’s just a consideration.
But I have another question:
we know that the MMSE should come to have the same performance of the ZF for high SNRs, because the noise term become less relevant.
So, why don’t the two curves (ZF_BER and MMSE_BER) merge?
Well, actually I do have an answer for this, but my supervisor was a little reluctant to accept it:
if you plot the two curves in linear scale they do merge… but in the log scale they run as parallels… I think that’s because the reason why they merge is the SNR ( in DB!!) linearly raising… but a a linear raising of a dB value means a line in log-scale, so there won’t be a slope changing to see the curves merge in log-scale..
Also even if the receiver is different (ZF or MMSE) the system still has some properties that stay the same in the two case: I refer two the slope of the BER curve which for a Ntx=Nrx system is one decade down in 10dB (no diversity, or diversity order = 1). This must be true for both the receivers… so if they have the same slope they won’t merge in log scale.
Let me know what do you think of what I’ve said and if you have some other explanation.
Please answer
I thank you in advance anyway
Best regards
Francesco
@Franseco: My replies
1/ I rechecked the equations for MMSE. The noise term is E{n*n^H}. The variance of real and imaginary arm of noise is 0.5*10^(-Eb_N0_dB/10). When we compute the noise power, we have to add the variances of real and imaginary term and the total variance is 10^(-Eb_N0_dB/10). Agree?
2/ Well, I also have difficulty accepting the linear vs dB hypothesis. Did you try running the curve for very high values of Eb_N0_dB? Lets say till 100dB?
You dont need “2*”. What you need is noise power => sigma_2 =No/2.
OK… then this means that in the formula for W we have No/2 instead of No as it’s currently written down ?
By the way, any ideas about the curves merging thing?
Thanks a lot in advance
Francesco
@Francesco: As explained earlier
Hello
i need a matlab code for a zero padding in OFDM and OLA in OFDM please could you help me !!
Best regards !!
@hildaa: Sorry, I do not have the Matlab code. Good luck.
Hello. Thanks for your posts.
I have a question.
Your simulation result shows that the MMSE equalizer has 3dB improvement than the ZF equalizer.
But, the reference book – [DIG-COMM-BARRY-LEE-MESSERSCHMITT] – shows MMSE detector outperforms ZF detector by 1.8dB from “10.3.9. Performance Comparison”.
Which one is correct?
When I simulated 2by2 MIMO system, the 4QAM-ZF system has same BER of BPSK-ZF system.
(Rayleigh ch./AWGN noise/BER vs SNR per bit per antenna(Eb/No)/4QAM=1/sqrt(2)*{+-1+-j})
But, there is a little difference(almost 1dB) between 4QAM-MMSE and BPSK-MMSE system.(4QAM is worse.) This shows that the MMSE outperforms ZF by 2dB at 4QAM system when the MMSE outperforms ZF by 3dB at BPSK system.
So, I am very confusing.
MMSE outperforms ZF by 2dB? 3dB?
4QAM-MMSE is worse than BPSK-MMSE?
Plz answer to me..
Thanks
@JH: The simulations which I did where comparing BPSK ZF with BPSK MMSE (and in the book, comparison is between QPSK-ZF with QPSK-MMSE). I am not sure that’s the reason for the difference in the performance.
Are you sure that QPSK MMSE is poorer than BPSK MMSE?
asd
i want a working code for 8-psk
@Maya: The code in 16PSK simulations can be easily adapted to 8PSK case
http://www.dsplog.com/2008/03/18/symbol-error-rate-for-16psk/
http://www.dsplog.com/2008/05/18/bit-error-rate-for-16psk-modulation-using-gray-mapping/
would u help me , sir
i need matlab code for
Signal s—a BPSK signal that takes the values of ±1 with equal probability—passes
through channel c which has the transfer function 1 + 0.5z -1. This means that the output
of the channel at instant ݅ is equal to s(i)+0.5s(i-1). Zero-mean white Gaussian noise
v(i) with variance sigma 2 is added to the channel’s output so that
Y(i)=s(i)+0.5s(i-1)+v(i)
The sequence ݏሺ݅ሻ is white and is independent of noise v.
A three-tap linear minimum mean square error (LMMSE) equalizer is used to estimate
S(i-) using the three samples y(i), y(i-1), y(i-2) . We will solve for the two
cases of = 0 and = 1. The LMMSE equalizer forms a linear combination of
y(i), y(i-1), y(i-2) to produce the scalar estimate s^(i- ) such that ሾ[s(i-
) –- s(i- )]2 is minimized.
Find the LMMSE equalizer’s coefficients for the two cases = 0 and =1. Note
that the coefficients are a function of sigma 2. Plot the mean square error for both cases of
on the same graph using the signal-to-noise ratio (SNR) at the input of the equalizer as
the horizontal axis. SNR in this problem is defined as the ratio of signal power to sigma 2at
the input of the equalizer. (Hint: you need to obtain the signal power at the output of the
channel to obtain the power of the signal to which the noise is added.) Plot over the SNR
range from 0 dB to 12 dB.
@mohamed: I have not discussed MMSE equalization in a multipath channel till now. Thats my upcoming post.
helo krishna
actualy m working on mimo synchronization
can u help me in preamble used in mimo ofdm system ?????
how can i create a preamble in a mimo system????
@Sadaf: In a MIMO system like 802.11n, the preamble defined in 802.11a case is extended to be used in MIMO links. The SISO preamble is multiplied by a orthogonal matrix which is known at the receiver.
You may find more details about it @
https://mentor.ieee.org/802.11/file/05/11-05-1102-04-000n-joint-proposal-phy-specification.doc
help me please :my question is I need the matlab program calculates the’binary ‘error rate ”BER”of systems (SISO, Simo, miso mimo) COMPARISON
@bouhafs: Please have a look at
http://www.dsplog.com/tag/diversity/
http://www.dsplog.com/tag/mimo
sir,
do u hve MATLAB code of MIMO MCCDMA
thanks for your your graet work
please I study for my master in frequency synchronization in mimo ofdm system but i have problem with the matlab code to simulate to find out if estimation of the CFO on one path is affected by the CFO values of the adjacent paths and examine the estimator accuracy in term of its mean and variance
thanks for your your graet work
please I study for my master in frequency synchronization in mimo ofdm system but i have problem with the matlab code to simulate to find out if estimation of the CFO on one path is affected by the CFO values of the adjacent paths and examine the estimator accuracy in term of its mean and variance
please help me it’s urgent and necessary
@eng_dina: How are you modeling the CFO for MIMO systems?
If all the chains have a common RF clock, then all the chains will have similar CFO and the estimate from all the chains can be combined to improve the accuracy of the CFO estimation.
If the chains have independent RF clock, then we need to estimate CFO on each chain independently.
Hi Krishna, I wrote a matlab program to estimate x through y (y=hx+n) where n is au gaussian noise. I used MMSE to find x through the formula xHat=h’*inv(h*h’+10^(-SNR/10)*I)*y. When i wanted to compare the Theoretical SNR at the receiver (||h||^2 E(x^2)/N0) to the SNR calculated at the output of the MMSE receiver ((1/MMSE)-1), i found the SNR at the output of MMSE is much more higher than the theoretical one. Do you have any explanation on this. Does it mean that the MMSE improve the SNR ? Thank you.
hello krishna sankar
do you have matlab code about soft quantization with known noise variance
(chi-square) in ofdm mobile radio system with bpsk modulation ?or any refferances or any site discuss about this?
For the MIMO case with MMSE detection, is the received Eb/No calibrated per transmitted stream? For example, when you go from the 1×2 case to the 2×2 case, does the noise variance stay the same, and you just add a second stream to a second antenna, of power equal to that of the original stream ? Thanks.
hi friends,i am doing my master thesis on LTE.i am trying to implement mmse and svd estimation but i have some probleme
this is my mmse code,could some onw help me?
i have my pdp and its length is L.
SNR = 1/(noiseVar);
beta=1;
power_delay_profile=[power_delay_profile zeros(1,fftlen-(L))];
power_delay_profile=fft(power_delay_profile);
power_delay_profile=diag(power_delay_profile(carriers));
% Calculate frequency correlation matrix
R_hh =power_delay_profile;
R_hy = R_hh + (((beta/SNR)* eye(length(carriers))));
% Calculate LMMSE estimate
h_LMMSE = (R_hh*inv(R_hy)) * h_LS;
please help!
this is the paper i refer http://www.sps.ele.tue.nl/members/C.K.Ho/paper/Robust%20MMSE%20channel%20estimation%20in%20OFDM%20systems%20with%20practical%20timing%20synchronization.pdf
@Usman: This equation is discussed in most of the classical text books. Please refer to Digital Communications by Barry, Lee, Messerschmit
@aydar: I agree.
@aydar: My replies:
a) I do not have a precise understanding of how MRC works in the case of 3 receive 2 transmit case. I am sure that having the ‘extra’ antenna at the receiver improves the diversity gain. However, am not sure what we are doing is MRC or not
b) Why do you say that there is interference in SIMO systems?
@aydar: What are CAZAC sequences? Can you please write more about it.
b) Well, at least in LTE we would always have an interference from other cells.
“…The CAZAC sequence is a sequence that has constant amplitude in both regions of time and frequency and it has always Zero Auto-Correlation for time shift that is other than the cyclic self-correlation value is 0. As the CAZAC sequence has Constant amplitude in a time region, it can keep PAPR (Peak to Average Power Ratio) low. As the CAZAC sequence also has Constant amplitude in a frequency region, it is a sequence suitable for propagation path estimation in the frequency region. Here, a small PAPR means that it can keep the power consumption low. This feature is preferred in the mobile communication. …”
Pilots for LTE specified in 3GPP 36.211.
@aydar: ok
@aydar: Thanks.