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Multiuser Detection for CDMASystems

Paper by A. Duel-Hallen, J.Holtzman, and Z. Zvonar, 1995.

Presented by Peter Ang

April 27, 2001.

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Outline

Overview of DS/CDMA systems

Concept of multiuser detection (MUD)

MUD algorithms

Limitations of MUD

Conclusion

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DS/CDMA Systems

A conventional DS/CDMA system treats each userseparately as a signal, with other users considered asnoise or MAI – multiple access interference

Capacity is interference-limited

Near/far effect: users near the BS are received at higher

powers than those far away those far away suffer a degradation in performance

Need tight power control

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Multiuser Detection

Multiuser detection considers all users as signals for eachother -> joint detection

Reduced interference leads to capacity increase

Alleviates the near/far problem

MUD can be implemented in the BS or mobile, or both

In a cellular system, base station (BS) has knowledge ofall the chip sequences

Size and weight requirement for BS is not stringent

Therefore MUD is currently being envisioned for the uplink(mobile to BS)

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Concept of MUD

Simplified system model (BPSK) Baseband signal for the kth user is:

•  x k (i) is the ith input symbol of the kth user

• c k (i) is the real, positive channel gain 

• sk (t) is the signature waveform containing the PN sequence

•   k  is the transmission delay; for synchronous CDMA, k=0 for allusers

Received signal at baseband

• K  number of users 

•  z(t) is the complex AWGN

0i

k k k k k    iT t  sici xt u     

 K 

k 

k    t  z t ut  y1

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Concept of MUD (2)

Sampled output of the matched filter for the kth user:

1st term - desired information

2nd term - MAI

3rd term - noise

Assume two-user case (K=2), and

 

 K 

k  j

T T 

k  jk  j jk k 

T 

k k 

dt t  z t  sdt t  st  sc x xc

dt t  st  y y

0 0

0

T 

dt t  st  sr 0

21

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Concept of MUD (3)

Outputs of the matched filters are:

Detected symbol for user k:

If user 1 is much stronger than user 2 (the near/far problem),

the MAI term rc 1 x 1 present in the signal of user 2 is very large Successive Interference Cancellation

decision is made for the stronger user 1:

subtract the estimate of MAI from the signal of the weaker user:

all MAI can be subtracted from user 2 signal provided estimate iscorrect

MAI is reduced and near/far problem is alleviated

211222122111    z  xrc xc y z  xrc xc y  

k k    y x sgnˆ  

211122

1122

ˆsgn

ˆsgnˆ

 z  x xrc xc

 xrc y x

11 sgnˆ   y x  

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MUD Algorithms

OptimalMLSE

Decorrelator MMSE

Linear 

Multistage Decision

-feedback

Successive

interference

cancellation

Non-linear 

Suboptimal

Multiuser 

Receivers

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Optimal MLSE Detector

Maximum-likelihood sequence estimation (MLSE) is theoptimal detector (Verdú, 1984)

For synchronous CDMA, search over 2K possiblecombinations of the bits in vector x

For asynchronous CDMA, use Viterbi algorithm with 2K-1 states

Both too complex for practical implementation

WRWbbWx y x  T T 

 x  K  2maxarg

ˆ

1,1

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Decorrelator

Matrix representation

where y =[y 1,y 2,…,y K ]T, R and W  are K xK  matrices

Components of R are given by cross-correlations between signaturewaveforms sk (t)

W  is diagonal with component W k,k  given by the channel gain c k  ofthe k th user

 z  is a colored Gaussian noise vector

Solve for x  by inverting R

Analogous to zero-forcing equalizers for ISI channels

Pros: Does not require knowledge of users’ powers 

Cons: Noise enhancement

 z  x RW  y  

k k 

  y x z  R xW  y R y ~sgnˆ ~ 11  

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Multistage Detectors

Decisions produced by 1st stage are 2nd stage:

and so on… 

1sgn2

1sgn2

1122

2211

 xrc y x

 xrc y x  

  

1,1 21   x x

   

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Decision-Feedback Detectors

Characterized by two matrix transformation: forward filterand feedback filter

Whitening filter yields a lower triangular MAI matrix

Performance similar to that of the decorrelator

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DFD Performance

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Successive Interference Cancellers

Successively subtracting off the strongest remaining signal Cancelling the strongest signal has the most benefit

Cancelling the strongest signal is the most reliablecancellation

An alternative called the Parallel Interference Cancellers simultaneously subtract off all of the users’ signals from allof the others

works better than SIC when all of the users are received withequal strength (e.g. under power control)

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Performance of MUD

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Performance of MUD (2)

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Limitations of MUD

Issues in practical implementation Processing complexity

Processing delay

Sensitivity and robustness

Limitations of MUD

Potential capacity improvements in cellular systems are notenormous but certainly nontrivial (2.8x upper bound)

Capacity improvements only on the uplink would only bepartly used anyway in determining overall system capacity

Cost of doing MUD must be as low as possible so that there is

a performance/cost tradeoff advantage

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Conclusion

There are significant advantages to MUD which are,however, bounded and a simple implementation is needed

Current investigations involve implementation androbustness issues

MUD research is still in a phase that would not justify to

make it a mandatory feature for 3G WCDMA standards Currently other techniques such as smart antenna seem to

be more promising