Parallel Interference Cancellation in beyond 3G multi-user and multi-antenna OFDM systems

David Sabater Dinter Research Group for RF Communications 2003-5-26 1

Parallel interference cancellation in beyond 3G multi-user and multi-antenna OFDM systems

David Sabater Dinter University of Kaiserslautern

[email protected]

Supervisor: A. Sklavos


summary

• service area based system in the uplink• transmission model• subcarrierwise investigation• optimum and suboptimum linear detection• parallel interference cancellation • PIC with improved estimate refinement • simulation results• conclusions


uplink transmission in a service area

MT

AP

CU

AP

AP

MT

MT

1d

2d

ˆ Kd

1d

2d

Kd


B B

1,1 ,1

1, 2 , 2

1, , B

(1)

(2)

( )B

(1)

(2)

( )

(1)

(2)

( )

K

K

K K K K KK

H H

H H

H H

dd

d

nn

n

ee

e

transmission model

• Additive noise vector at AP :• Received signal vector at AP :

Bk

Bk

B B B F( ) ( ,1) ( , ) T( )k k k Nn nn B B B F( ) ( ,1) ( , ) T( )k k k Ne ee

B F FK N KN F 1KN B F 1K N B F 1K N

• data symbol vector sent by MT k: F( , )(k) ( ,1) T( )k Nkd dd


subcarrierwise investigation

F

1

2

N

H 0 0

0 H 0H

0 0 H

B B B

1,1 2,1 ,1

1,2 2,2 ,2

1, 1, ,

K

K

K K K K

H H H

H H HH

H H H

B F F block diagonal matrixK N xKN

•conversion of totalsystem to smaller parallel systems•significant effort reduction in

•linear ZF, MMSE•non linear MLVE

FN

B F F matrixsparseK N xKN


F

F

F FF F

2

all data vectors equiprobable and Gauss noise maximum likehood vector estimator (MLVE)

ˆ arg minn

n

K

n nn n

d

d

d e H d

D

optimum non-linear and suboptimum linear detector

F

F F Fˆ arg max |n

n n n

KP

dd d e

D

F F F( ) ( ) ( )ˆ .n n nd D e

F F F F1( ) ( )*T ( ) ( )*Tn n n n

D H H H

• Optimum multiuser detector,

• suboptimum linear detection

• Example: ZF criterion FF F( )( ) ( )

2min

nn n

e H d


parallel interference cancellation

1*T

*T

diag

diag

F H H

R H H

*TH

e rF

R

pd

ˆ 1p d-bank

of MF

ˆ pu

iterative MUD

estim

ate

refin

emen

tan

d FE

C d

ecod

ing

• Forward matrix:

• Feedback matrix:


no estimate refinement

F, , (non zero) eigenvalues of KN FR

• no estimate refinement,

• PIC convergent if

• convergence value

ˆ ˆd d

1 FR

d FECdemodd

u

d

estimate refinement and FEC decoding

spectral radius

FFR max , , KN

ZFˆ ˆ( )=d d


spectral radius example

2,...,8K

P R

divergenceconvergence

• • exp. Channel snapshot

B 8K

R


estimate refinement by hard quantization

d FECdemodd

u

d


• exploit knowledge of discrete

• quantization of to the modulation constellation

F( , )k nd Dd

F

2ˆ ˆarg min ( )KN

p

d

d d dD

D


estimate refinement by soft quantization

d FECdemod u

d


2

min Ek

km md d

kmd must satisfy

estim.

2d

2dˆ2

tanh 2

sign

mod

ˆk km mL d d


-10 -5 0 5 10 15 20

10-3

10-2

10-1

100

simulation results

F( ) 1n

• • • subc. • exp. channel • no quant.

4K B 4K

10 b 010log / / dBE N

bP

AWGN ZF

PICMF


-10 -5 0 5 10 15 20

10-3

10-2

10-1

100

simulation results

F( ) 1n

10 b 010log / / dBE N

bP

• • • subc. • exp. channel• hard quant.

4K B 4K

AWGN

ZF

PICMF


-10 -5 0 5 10 15 20

10-3

10-2

10-1

100

simulation results

F( ) 1n

10 b 010log / / dBE N

bP

• • • subc. • exp. channel • soft quant.

4K B 4K

AWGN

ZF

PICMF


-10 -5 0 5 10 15 20

10-3

10-2

10-1

100

simulation results

bP

10 b 010log / / dBE N

• • • subc. • exp. channel • no quant.

2K B 4K

F( ) 1n

AWGN PIC(even iterations)

PIC(odd iterations)

ZF


PIC with improved estimate refinement

*TH

e rF

1d

bank of MF

iterative MUD

first iterationsecond iterationthird iteration

*TH

e rF

R

2d

-bank of MF

iterative MUD

demod

ˆ 2u

ˆ 1d

*TH

e rF

R

3d

-bank of MF

iterative MUD

demod

ˆ 3u

ˆ 2dhard Q

or soft Q

• principle: input MAI-free at quantization process• starting estimate refinement at the third iteration, errors

introduced by quantization method can be reduced

d


-10 -5 0 5 10 15 20

10-3

10-2

10-1

100

simulation results

10 b 010log / / dBE N

bP

• • • subc. • exp. channel • hard quant.• hard mod. quant.

2K B 4K

F( ) 1n

AWGN

MF

ZF

PICPIC mod


-10 -5 0 5 10 15 20

10-3

10-2

10-1

100

simulation results

10 b 010log / / dBE N

bP

• • • subc. • exp. Channel• soft quant.• soft mod. quant.

2K B 4K

F( ) 1n

AWGN

MF

ZF

PICPIC mod


-10 -5 0 5 10 15 20

10-3

10-2

10-1

100

simulation results

10 b 010log / / dBE N

bP

• • • • exp. Channel• hard quant.• hard mod. quant.

3K B 6K F 32N

AWGN

MF

ZF

PICPIC mod


-10 -5 0 5 10 15 20

10-3

10-2

10-1

100

simulation results

10 b 010log / / dBE N

bP

• • • • exp. Channel• soft quant. • soft mod. quant.

3K B 6K F 32N

AWGN

MF

ZF

PIC modPIC


conclusions

• PIC is a flexible JD scheme • PIC is not always convergent• performance improvement with modified estimate refinement• more investigation towards PIC necessary

Parallel Interference Cancellation in beyond 3G multi-user and multi-antenna OFDM systems

Technology