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Multiuser Detection (MUD)Combined with array signal processing

in current wireless communication environments

2002.4.10.Wed.

박사 3 학기 구 정 회

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Contents Introduction Motivation Definition System model Basic algorithms Space-time multiuser detection Current research issues Proposals Conclusion

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Introduction Multiple access interference (MAI)

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

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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

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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

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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

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Synchronous system model (1/2)

Baseband received signal

Output of the kth user’s correlator

1

( ) ( ) ( ) ( ) ( )K

k k kk

r t A t g t b t n t

,1

,

1( ) ( )

1 ( ) ( )

1( ) ( )

b

b

b

T

k kb

K T

k k i k i i ki bi k

k k k k

T

i k i kb

y r t g t dtT

A b Ab n t g t dtT

A b MAI z

g t g t dtT

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Synchronous system model (2/2)

Three user synchronous system : Matrix-vector system model

1 1 1 2,1 2 2 3,1 3 3 1

2 1,2 1 1 2 2 3,2 3 3 2

3 1,3 1 1 2,3 2 2 3 3 3

y Ab A b A b z

y Ab A b A b z

y Ab A b A b z

1 1 12,1 3,1 1

2 1,2 3,2 2 2 2

1,3 2,3 33 3 3

1 0 0

1 0 0

1 0 0

y b zA

y A b z

Ay b z

y RAb z

R I Q

y Ab QAb z

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Asynchronous system model

Baseband received signal

1

( ) ( ) ( ) ( ) ( )K

k k k k kk

r t A t g t b t n t

2,1

1,2 3,2

2,3 4,3

3,4 5,4

4,5 6,5

5,6

1 0 0 0 0

1 0 0 0

0 1 0 0

0 0 1 0

0 0 0 1

0 0 0 0 1

R

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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

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Optimal multiuser detector Solution to the ML problem

Combinatorial quadratic minimization : NP-hard problem Only the exhaustive search will guarantee the global mini

mum

{ 1,1}

1

2

1

{ 1,1}

{ 1,1}

ˆ arg max ( | )

( ) ( )( | ) exp( )

2ˆ arg min ( ) ( )

arg min 2Re{ }

K

K

K

MLb

H

o

HML

b

T T

b

b p y b

y Rb R y Rbp y b K

b y Rb R y Rb

b Rb b y

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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

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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

1

1

( 2Re{ })2 2 0

T T

T

dec dec

dec

b Rb b yRb y

b

Rb y b R y

L R

1 1dec decb R y Ab R z Ab z

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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 detctor2 2( ) [| | ] [| | ]MMSEJ L E b b E Ly b

2 10[ ( / 2) ]MMSEL R N A

MMSE MMSEb L y

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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)

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SIC

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

Takes serial approach to canceling interference

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PIC

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

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Performance comparison of SIC and PIC

Comparison

Major disadvantage of nonlinear detectors Dependence on reliable estimates of the

received amplitudes

PIC SIC

AdvantagesIn a well power- controlled environments

In fading environments

Disadvantages

Requires more hardware

Problem of power reordering,Large delay

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

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

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Space-time MUD (1/7)

Problem of MUD in multipath CDMA channels with receiver antenna array

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Space-time MUD (2/7)

Signal model1

0

1

0

1

,1 ,

1

[1] ( ) ( ) ( ), 1, ,

[2] ( ) ( ) ( ), 0

[3] ( ) ( )

[4] [ ]

[5] ( ) ( ) ( ) ( )

( )

M

k k k ki

N

k k cj

L

k kl kl kll

Tkl kl kl P

K

k kk

k k kll

x t A b i s t iT k K

s t c j t jT t T

h t a g t

a a a

r t x t h t n t

A b i a

1

0 1 1

1

( ) ( )

[6] ( ) [ ( ) ( )]

M K L

kl k kli k

TP

g s t iT n t

n t n t n t

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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,1

0 1 1

1

( ; ) ( ) ( )

( ) [ ( ) ( )] , [ (0) ( 1) ]

M K L

k k kl kl k kli k l

T T T TK

S t b A b i a g s t iT

b i b i b i b b b M

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Space-time MUD (4/7) Likelihood function of the received

waveform conditioned on all the transmitted symbols of all users b

1

0 1

( )

*

1( )

( ; ) ( ) ( )

( ) ( )

k

kl

M KH

k ki k

y i

LH

kl kl k kll

z i

S t b r t dt A b i

g a r t s t iT dt

2

2

({ ( ) : } | ) exp[ ( ) / ]

( ) 2Re{ ( ; ) ( ) } | ( ; ) |H

l r t t b C b

b S t b r t dt S t b dt

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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

{ ( ) :1 ,0 1}ky i k K i M

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

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Space-time MUD (7/7)

Simulation environments 2 users, 2 multipath/user PG=128, 8 array elements array, SNR=-

20dB

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Current research issues (1/2)

Choice for a practical MUD algorithm Complexity Performance

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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

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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

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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