1. Multiuser Detection (MUD) Combined with array signal processing in current wireless communication environments
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2. Contents Introduction Motivation Definition System model
Basic algorithms
Space-time multiuser detection
Current research issues
Proposals
Conclusion

3. Introduction Multiple access interference (MAI) Multiuser detection
Interference between direct-sequence user
A factor which limits the capacity and performance of DS-CDMA system
As the number of interferers or their power increases, MAI becomes substantial
Multiuser detection
Area of research with potential to significantly improve DS-CDMA communication
Combination with array signal processing

4. Motivation (1/2) Conventional detection
Matched filtering + Sampling of the received signal + Decision device
In a single path transmission environment
Optimal in the sense that the SNR is maximized
Maximum-likelihood (ML) detection
In a multiuser environment
The SNR is still maximized
Not ML due to the presence of MAI

5. Motivation (2/2) CDMA is interference-limited system : NO !
Conventional detector is interference-limited : Sub-optimal approach
Conventional detector does not take into account the existence of MAI
Verdu showed that “ It is the thermal noise and not the MAI that rules the ultimate performance levels attainable in a CDMA system”
Is it possible to exploit the particular structure of the MAI ? : YES !
Note
Conventional detector (output of a bank of matched filter) provides a minimal sufficient statistics for detection
Asynchronous CDMA system은 MAI외에, 채널에 의한 ISI가 생기는 dispersive CDMA system의 특별한 경우로 볼 수 있다.

6. Definition
Code and timing information of multiple users are jointly used to better detect each individual user
Important assumption
The codes and timing information of the multiple users are known to the receiver a priori

7. Synchronous system model (1/2)
Baseband received signal
Output of the kth user’s correlator

8. Synchronous system model (2/2)
Three user synchronous system : Matrix-vector system model

9. Asynchronous system model
Baseband received signal

10. Basic algorithms Optimal multiuser detector
Maximum likelihood sequence detector (’86, Verdu)
Sub-optimal multiuser detectors
Linear multiuser detectors
Subtractive interference cancellation detectors
Trade-off between complexity and performance

11. Optimal multiuser detector
Solution to the ML problem
Combinatorial quadratic minimization : NP-hard problem
Only the exhaustive search will guarantee the global minimum

12. Linear multiuser detector
Basic principle
Apply a linear mapping, L, to the soft output of the conventional detector to reduce the MAI seen by each user
Decorrelating detector
MMSE detector

13. Decorrelating detector
Applies the inverse of the correlation matrix
Soft estimate of the detector
All the MAI has been removed at the expense of noise enhancement
Unconstrained, quadratic minimization problem

14. MMSE detector
Take into account the background noise and utilizes knowledge of the received signal
Linear mapping which minimizes the cost function
Soft estimate of the MMSE detctor

15. Subtractive interference cancellation
Basic principle
The creation at the receiver of separate estimates of the MAI contributed by each user
Successive interference cancellation (SIC)
Parallel interference cancellation (PIC)

16. SIC Takes serial approach to canceling interference
Implementation difficulties
One additional bit delay is required per stage of cancellation
There is a need to re-order the signals whenever the power profile changes

17. PIC
Estimate and subtracts out all of the MAI for each user in parallel

18. Performance comparison of SIC and PIC
Major disadvantage of nonlinear detectors
Dependence on reliable estimates of the received amplitudes
PIC
SIC
Advantages
In a well power- controlled environments
In fading environments
Disadvantages
Requires more hardware
Problem of power reordering,
Large delay

19. Performance analysis (1/2) (With perfect power control)

20. Performance analysis (2/2) (Flat Rayleigh fading with channel est.)

21. Space-time MUD (1/7)
Problem of MUD in multipath CDMA channels with receiver antenna array

22. Space-time MUD (2/7)
Signal model

23. Space-time MUD (3/7) Sufficient statistic
Summarizes the useful information that a measurement brings about a parameter
Find that for demodulating the multiuser symbols from the space-time signal
Define the following,

24. Space-time MUD (4/7)
Likelihood function of the received waveform conditioned on all the transmitted symbols of all users b
Cameron-Martin formula

25. Space-time MUD (5/7)
Sufficient statistic for detecting the multiuser symbol b :
How to obtain ?
Passing the received signal through (KL) beamformers directed to each path of each user’s signal, followed by a bank of K maximum-ratio multipath combiners (i.e. RAKE receiver)
Sufficient statistic = Output of space-time matched filter
Beamformer is a spatial matched filter
RAKE receiver is a temporal matched filter

26. Space-time MUD (6/7) (Receiver structure)

27. Space-time MUD (7/7) Simulation environments 2 users, 2 multipath/user
PG=128, 8 array elements array, SNR=-20dB

28. Current research issues (1/2)
Choice for a practical MUD algorithm
Complexity
Performance
Spatially and temporally noise-whitening receiver structure is developed

29. Current research issues (2/2)
System design choices
If MUD is to be part of the next standard, some minimum performance requirements have to be specified
MUD research is still in in a phase that would not justify making it a mandatory feature for wideband CDMA standards
Even though MUD is a receiver technique, it might have an impact on the system design because of its large complexity, while a proper system design might ease the implementation of the MUD

30. Proposals
Subspace-based blind adaptive detector with lower computational complexity, robustness against signature waveform mismatch, non-Gaussian noise, impulsive noise
Blind receiver for multiuser detection in unknown correlated noise
Dual mode multiuser detector that dynamically switches its detection mode between matched-filter and decorrelator operations based on the channel characteristics

31. Conclusion
Current wireless communication environments require considerable signal-processing ‘intelligence’
Two categories of many advanced signal processing techniques are multiuser detection and space-time processing
Combined multiuser detection and array processing methods can ‘substantially’ outperform the conventional detector