1. A Novel Soft MIMO Detector for MIMO-OFDM (802.11n) Receivers
June 2005
doc.: IEEE /0559r0
July 2005
A Novel Soft MIMO Detector for MIMO-OFDM (802.11n) Receivers
Date:
Author
Name
Company
Address
Phone
Behrouz Farhang-Boroujeny
Univ of Utah
Sal Lake City, UT 84112
summary deck
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Behrouz Farhang-Boroujeny, Univ of Utah
Marc de Courville and Tim Wakeley

2. Outline Our solution to LLR computation Simulation results
June 2005
doc.: IEEE /0559r0
July 2005
Outline
Introduction
Channel model
Soft Information: Log-likelihood ratio, LLR values
what is the problem?
Zero-forcing / MMSE / VBLAST detectors
computation of LLR values
Our solution to LLR computation
Simulation results
Conclusions
Behrouz Farhang-Boroujeny, Univ of Utah
Marc de Courville and Tim Wakeley

3. We answer the following question:
June 2005
doc.: IEEE /0559r0
July 2005
Introduction
We answer the following question:
In a MIMO set-up how one can efficiently obtain soft information, e.g., log-likelihood ratio (LLR) values, of the data bits?
Behrouz Farhang-Boroujeny, Univ of Utah
Marc de Courville and Tim Wakeley

4. We answer the following question:
June 2005
doc.: IEEE /0559r0
July 2005
Introduction
We answer the following question:
In a MIMO set-up how one can efficiently obtain soft information, e.g., log-likelihood ratio (LLR) values, of the data bits?
The material presented here are protected by a patent application owned by the university of Utah.
Behrouz Farhang-Boroujeny, Univ of Utah
Marc de Courville and Tim Wakeley

5. Channel Model We consider: A frequency selective channel.
June 2005
doc.: IEEE /0559r0
July 2005
Channel Model
We consider:
A frequency selective channel.
OFDM is used to convert the frequency selective channel to a number of parallel flat fading channels.
Accordingly, each subcarrier channel has the following model:
y = Hd+n
where
d is a vector of transmit symbols
y is a vector of received signal
H is the channel gain matrix
n is an additive noise vector.
Behrouz Farhang-Boroujeny, Univ of Utah
Marc de Courville and Tim Wakeley

6. Receiver Structure MIMO Detector Channel Decoder June 2005
doc.: IEEE /0559r0
July 2005
Receiver Structure
MIMO Detector
Channel Decoder
Behrouz Farhang-Boroujeny, Univ of Utah
Marc de Courville and Tim Wakeley

7. Receiver Structure We address an implementation of this block
June 2005
doc.: IEEE /0559r0
July 2005
Receiver Structure
We address an implementation of this block
MIMO Detector
Channel Decoder
Behrouz Farhang-Boroujeny, Univ of Utah
Marc de Courville and Tim Wakeley

8. June 2005
doc.: IEEE /0559r0
July 2005
Receiver Structure
Feedback from the output of the channel decoder to MIMO detector, allows near capacity performance.
MIMO Detector
Channel Decoder
Behrouz Farhang-Boroujeny, Univ of Utah
Marc de Courville and Tim Wakeley

9. Soft Information: Log-likelihood ratio (LLR) values
June 2005
doc.: IEEE /0559r0
July 2005
Soft Information: Log-likelihood ratio (LLR) values
Symbol vector d is obtained from a vector b=[b1 b2 … bN] of information bits through some mapping.
Behrouz Farhang-Boroujeny, Univ of Utah
Marc de Courville and Tim Wakeley

10. Soft Information: Log-likelihood ratio (LLR) values
June 2005
doc.: IEEE /0559r0
July 2005
Soft Information: Log-likelihood ratio (LLR) values
Symbol vector d is obtained from a vector b=[b1 b2 … bN] of information bits through some mapping.
We wish to calculate
where
is calculated in the same way.
Behrouz Farhang-Boroujeny, Univ of Utah
Marc de Courville and Tim Wakeley

11. Soft Information: Log-likelihood ratio (LLR) values
June 2005
doc.: IEEE /0559r0
July 2005
Soft Information: Log-likelihood ratio (LLR) values
Symbol vector d is obtained from a vector b=[b1 b2 … bN] of information bits through some mapping.
We wish to calculate
where
is calculated in the same way.
Problem: the number of combinations that b-k takes is 2N-1!
Behrouz Farhang-Boroujeny, Univ of Utah
Marc de Courville and Tim Wakeley

12. Soft Information: Log-likelihood ratio (LLR) values
June 2005
doc.: IEEE /0559r0
July 2005
Soft Information: Log-likelihood ratio (LLR) values
Symbol vector d is obtained from a vector b=[b1 b2 … bN] of information bits through some mapping.
We wish to calculate
where
is calculated in the same way.
Problem: the number of combinations that b-k takes is 2N-1!
The key point here is that most of the terms in the numerator and denominator are insignificant.
Thus, a handful of the significant terms may be sufficient for accurate estimation of k.
Behrouz Farhang-Boroujeny, Univ of Utah
Marc de Courville and Tim Wakeley

13. Log-likelihood ratio values: max-log algorithm
June 2005
doc.: IEEE /0559r0
July 2005
Log-likelihood ratio values: max-log algorithm
Behrouz Farhang-Boroujeny, Univ of Utah
Marc de Courville and Tim Wakeley

14. Log-likelihood ratio values: max-log algorithm
June 2005
doc.: IEEE /0559r0
July 2005
Log-likelihood ratio values: max-log algorithm
This incurs an insignificant loss (in the order of a a fraction of 1 dB) in performance.
Behrouz Farhang-Boroujeny, Univ of Utah
Marc de Courville and Tim Wakeley

15. Zero-forcing / MMSE / VBLAST detectors
June 2005
doc.: IEEE /0559r0
July 2005
Zero-forcing / MMSE / VBLAST detectors
Zero-forcing detector:
Estimate of d = Q[(H*H)-1H*y]
Behrouz Farhang-Boroujeny, Univ of Utah
Marc de Courville and Tim Wakeley

16. Zero-forcing / MMSE / VBLAST detectors
June 2005
doc.: IEEE /0559r0
July 2005
Zero-forcing / MMSE / VBLAST detectors
Zero-forcing detector:
Estimate of d = Q[(H*H)-1H*y]
MMSE detector:
Estimate of d = Q[(H*H+2I)-1H*y]
Behrouz Farhang-Boroujeny, Univ of Utah
Marc de Courville and Tim Wakeley

17. Zero-forcing / MMSE / VBLAST detectors
June 2005
doc.: IEEE /0559r0
July 2005
Zero-forcing / MMSE / VBLAST detectors
Zero-forcing detector:
Estimate of d = Q[(H*H)-1H*y]
MMSE detector:
Estimate of d = Q[(H*H+2I)-1H*y]
VBLAST/Successive Interference Canceller (SIC) detector:
Detects the strongest symbol first, subtract the detected symbol, and continue with the successive detection and cancellation of the rest of the symbols.
Behrouz Farhang-Boroujeny, Univ of Utah
Marc de Courville and Tim Wakeley

18. Zero-forcing / MMSE / VBLAST detectors: computation of LLR values
June 2005
doc.: IEEE /0559r0
July 2005
Zero-forcing / MMSE / VBLAST detectors: computation of LLR values
Starting with the detected d, for a chosen bit bk, it is identified that bk belongs to which element of d, say di.
Behrouz Farhang-Boroujeny, Univ of Utah
Marc de Courville and Tim Wakeley

19. Zero-forcing / MMSE / VBLAST detectors: computation of LLR values
June 2005
doc.: IEEE /0559r0
July 2005
Zero-forcing / MMSE / VBLAST detectors: computation of LLR values
Starting with the detected d, for a chosen bit bk, it is identified that bk belongs to which element of d, say di.
All the elements of d, except di, are kept fixed. The symbol di is then given all possible values from the symbol constellation, and from all these choices, the maximum values of P(bk=+1|y,d-i) and P(bk=-1|y,d-i) are found and substitute in the max-log LLR formula
Behrouz Farhang-Boroujeny, Univ of Utah
Marc de Courville and Tim Wakeley

20. June 2005
doc.: IEEE /0559r0
July 2005
Our solution to LLR computation: Markov chain Monte Carlo (MCMC) simulation technique
y=Ad+n,
State d3 d2 d1 S0 -1
S1` +1 S2 S3 S4 S5 S6 S7
Behrouz Farhang-Boroujeny, Univ of Utah
Marc de Courville and Tim Wakeley

21. June 2005
doc.: IEEE /0559r0
July 2005
Our solution to LLR computation: Markov chain Monte Carlo (MCMC) simulation technique
y=Ad+n,
State d3 d2 d1 S0 -1
S1` +1 S2 S3 S4 S5 S6 S7
This procedure gives us a set of selections of d that result in small distances |y-Ad(n)|.
These may be viewed as important samples of d that correspond to significant terms in the LLR equation
or its max-log version.
Behrouz Farhang-Boroujeny, Univ of Utah
Marc de Courville and Tim Wakeley

22. June 2005
doc.: IEEE /0559r0
July 2005
Our solution to LLR computation: Markov chain Monte Carlo (MCMC) simulation technique
y=Ad+n,
This procedure gives us a set of selections of d that result in small distances |y-Ad(n)|.
These may be viewed as important samples of d that correspond to significant terms in the LLR equation
or its max-log version.
State d3 d2 d1 S0 -1
S1` +1 S2 S3 S4 S5 S6 S7
If implemented in some clever way, the number of samples that is required for estimation of each k is in the order of 10 to 30, even though the size of the state space can be in the order of billions.
Behrouz Farhang-Boroujeny, Univ of Utah
Marc de Courville and Tim Wakeley

23. June 2005
doc.: IEEE /0559r0
July 2005
How Complex is MCMC?
It turns out that MCMC can be implemented VERY efficiently.
MCMC simulator for a MIMO channel with 4 transmit antenna and 16 QAM symbols has a complexity that is comparable or lower than that of a 16 bit-by-16 bit multiplier.
An implementation of this MCMC simulator on FPGA requires 600 slices.
Behrouz Farhang-Boroujeny, Univ of Utah
Marc de Courville and Tim Wakeley

24. Simulation Results MIMO-OFDM FFT size: 64 Cyclic prefix length: 16
June 2005
doc.: IEEE /0559r0
July 2005
Simulation Results
MIMO-OFDM
FFT size: 64
Cyclic prefix length: 16
Channel is estimated through pilot symbols transmitted at the beginning of each frame
Channel: convolutional code with polynomials [133, 171], R = 3/4
Behrouz Farhang-Boroujeny, Univ of Utah
Marc de Courville and Tim Wakeley

25. Simulation Results June 2005 doc.: IEEE 802.11-05/0559r0 July 2005
Behrouz Farhang-Boroujeny, Univ of Utah
Marc de Courville and Tim Wakeley

26. Simulation Results June 2005 doc.: IEEE 802.11-05/0559r0 July 2005
Behrouz Farhang-Boroujeny, Univ of Utah
Marc de Courville and Tim Wakeley

27. Simulation Results June 2005 doc.: IEEE 802.11-05/0559r0 July 2005
Behrouz Farhang-Boroujeny, Univ of Utah
Marc de Courville and Tim Wakeley

28. Simulation Results June 2005 doc.: IEEE 802.11-05/0559r0 July 2005
Behrouz Farhang-Boroujeny, Univ of Utah
Marc de Courville and Tim Wakeley

29. Simulation Results June 2005 doc.: IEEE 802.11-05/0559r0 July 2005
Behrouz Farhang-Boroujeny, Univ of Utah
Marc de Courville and Tim Wakeley

30. Simulation Results June 2005 doc.: IEEE 802.11-05/0559r0 July 2005
Behrouz Farhang-Boroujeny, Univ of Utah
Marc de Courville and Tim Wakeley

31. Wireless Communications Lab at ECE Dept of Univ of Utah
June 2005
doc.: IEEE /0559r0
July 2005
Wireless Communications Lab at ECE Dept of Univ of Utah
We are actively involved in development of MIMO detection techniques
In collaboration with L-3 Communication West in Salt Lake City, we have developed a MIMO testbed with 4 transmit and 4 receive antennae
A new version of our testbed that facilitates our research on MIMO detectors is under development.
We are open and seeking collaboration with industry. In particular, we are looking forward to any collaboration with IEEE n consortia.
Behrouz Farhang-Boroujeny, Univ of Utah
Marc de Courville and Tim Wakeley

32. June 2005
doc.: IEEE /0559r0
July 2005
Conclusions
The problem of soft estimation of information bits in a MIMO setup was addressed.
Using Markov chain Monte Carlo simulation technique, in the Wireless Communications lab of UofU, we have developed a very efficient detector for this task.
The proposed method could be used along with any conventional detector (ZF/MMSE/VBLAST- SIC) to improve its performance.
Gains in the order of 6 dB or more have been observed.
The proposed method is an excellent choice in systems that employ advanced channel coding, i.e., turbo and LDPC codes.
The proposed technology is extremely hardware friendly. The complexity of the MCMC simulator is not greater than a 16 bit-by-16 bit multiplier. Therefore, in a MIMO-OFDM where many subcarrier channels have to be examined in parallel, a number of MCMC simulators can be run in parallel at a minimum cost.
Behrouz Farhang-Boroujeny, Univ of Utah
Marc de Courville and Tim Wakeley

33. Publications June 2005 doc.: IEEE 802.11-05/0559r0 July 2005
[1] B. Farhang-Boroujeny, H. Zhu, and Z. Shi, “Markov chain Monte Carlo algorithms for CDMA and MIMO communication systems,” IEEE Trans. Signal Processing, Accepted for publication.
[2] H. Zhu, B. Farhang-Boroujeny, and R-R. Chen, “On performance of sphere decoding and Markov chain Monte Carlo detection methods,” IEEE Signal Processing Letters, Accepted for publication.
[3] R-R. Chen, B. Farhang-Boroujeny and A. Ashikhmin, “Capacity-approaching LDPC codes based on Markov chain Monte Carlo MIMO detection,” Submitted to IEEE Communications Letters, March 2005.
[4] H. Zhu, Z. Shi, and B. Farhang-Boroujeny, “MIMO detection using Markov chain Monte Carlo techniques for near-capacity performance,” Int. Conf. Acoustics, Speech and Signal Processing, ICASSP’05, Philadelphia, March 18 – 23, 2005.
[5] Z. Shi, Haidong Zhu, and B. Farhang-Boroujeny, Markov chain Monte Carlo techniques in iterative detectors: a novel approach based on Monte Carlo integration, IEEE Global Telecommunications Conference, GLOBECOM'04., vol. 2 , 29 Nov.-3 Dec., 2004, pp. 325 – 329.
[6] H. Zhu, B. Farhang-Boroujeny, and R-R. Chen, “On performance of sphere decoding and Markov chain Monte Carlo detection methods,” SPAWC 2005, the sixth IEEE International Workshop on Signal Processing Advances for Wireless Communications, June 5-8, 2005, Invited.
[7] R-R. Chen, B. Farhang-Boroujeny and A. Ashikhmin, “Capacity-approaching LDPC codes based on Markov chain Monte Carlo MIMO detection,” SPAWC 2005, the sixth IEEE International Workshop on Signal Processing Advances for Wireless Communications, June 5-8, 2005.
Behrouz Farhang-Boroujeny, Univ of Utah
Marc de Courville and Tim Wakeley