Transcript Combined Multiuser Detection and Channel Decoding with Receiver Diversity

Combined Multiuser Detection
and Channel Decoding
with Receiver Diversity
IEEE GLOBECOM
Communications Theory Mini-Conference
Sydney, Australia
November 10, 1998
VIRGINIA POLYTECHNIC INSTITUTE & STATE UNIVERSITY
MPRG
MOBILE & PORTABLE RADIO RESEARCH GROUP
Matthew C. Valenti and Brian D. Woerner
Mobile and Portable Radio Research Group
Virginia Tech
Blacksburg, Virginia
Virginia
1872
Tech
VIRGINIA POLYTECHNIC INSTITUTE
AND STATE UNIVERSITY
Outline of Talk


Outline



Multiuser detection for TDMA systems.
Macrodiversity combining for TDMA.
Turbo-MUD for convolutionally coded asynchronous
multiple-access systems.
Proposed System.
The Log-MAP algorithm.



For decoding convolutional codes.
For performing MUD.
Simulation results for fading channels.
Multiuser Detection
for the TDMA Uplink

For CDMA systems:
MUD for TDMA




For TDMA systems:




Resolvable interference comes from within the same cell.
Each cochannel user has a distinct spreading code.
Large number of (weak) cochannel interferers.
Cochannel interference comes from other cells.
Cochannel users do not have distinct spreading codes.
Small number of (strong) cochannel interferers.
MUD can still improve performance for TDMA.


Signals cannot be separated based on spreading codes.
Delay, phase, and signal power can be used.
Macrodiversity Combining
for the TDMA Uplink
Macrodiversity

BS 1

MS 1
BS 3

MS 3
MS 2
BS 2

In TDMA systems, the cochannel
interference comes from adjacent
cells.
Interferers to one BS are desired
signals to another BS.
Performance could be improved if
the base stations were allowed to
share information.
If the outputs of the multiuser
detectors are log-likelihood ratios,
then adding the outputs improves
performance.
Macrodiversity Combiner

Each of M base stations has a multiuser detector.
Macrodiversity

Each MUD produces a log-likelihood ratio of the code bits.
 i ,m  log

Pbi ,m  1 | y m 
Pbi ,m  1 | y m 
The LLR’s are added together prior to the final decision.
y1
Multiuser
Estimator
#1
Λ1
Λ
yM
Multiuser
Estimator
#M
ΛM
b̂
Turbo Multiuser Detection

Turbo MUD

Most TDMA systems use forward error correction
(FEC) coding.
The process of multiuser detection and FEC can be
combined using iterative processing.


“Turbo-MUD”
This is analogous to the decoding of serially
concatenated turbo codes, where:


The “outer code” is the convolutional code.
The “inner code” is an MAI channel.

The MAI channel can be thought of as a time varying
convolutional code with complex-valued coefficients.
Turbo MUD: System Diagram
d1
“multiuser interleaver”
Convolutional
Encoder
#1
interleaver #1
b1
Turbo MUD
MUX
dK
Convolutional
Encoder
#K
interleaver #K
y
SISO
MUD
MAI
Channel
n(t)
AWGN
bK
Λ (q )
APP
b
Ψ (q )
multiuser
interleaver
multiuser
deinterleaver
Turbo
MUD
Λ (q ')
Ψ(q ')
Bank of
K SISO
Decoders
dˆ ( q )
Estimated
Data
Turbo MUD w/ Macrodiversity
Macrodiversity Combining for
Coded TDMA Systems




Each base station has a multiuser estimator.
Sum the LLR outputs of each MUD.
Pass through a bank of Log-MAP channel decoder.
Feed back LLR outputs of the decoders.
y1
yM
Multiuser
Estimator
#1
Multiuser
Estimator
#M
Ψ1( q )
z (q)
)
Ψ (q
M
Bank of
K SISO
Channel
Decoders
Λ (q )
Ω (q )
dˆ ( q )
The Log-MAP Algorithm

The Viterbi Algorithm can be used to implement:
Log-MAP Algorithm




The MUD (Verdu, 1984).
The convolutional decoder.
However, the outputs are “hard”.
The iterative processor requires “soft” outputs.


In the form of a log-likelihood ratio (LLR).
The symbol-by-symbol MAP algorithm can be used.


The Log-MAP algorithm is performed in the Log domain,



Bahl, Cocke, Jelinek, Raviv, 1974. (BCJR Algorithm)
Robertson, Hoeher, Villebrun, 1997.
More stable, less complex than BCJR Algorithm.
We use Log-MAP for both MUD and FEC.
MAI Channel Model

Received signal at base station m:
K
rm (t )   sk ,m (t )  nm (t )
k 1
Log-MAP MUD
L
sk ,m (t )   Pk ,m [i ]bk [i ]a (t  iT   k ,m )e
j k , m
i 1

Where:

a is the signature waveform of all users.




Assumed to be a rectangular pulse.
k,m is a random delay of user k at receiver m.
Pk,m[i] is power at receiver m of user k’s ith bit.
Matched filter output for user k at base station m:
[i ]   r (t )a(t  iT  ˆ

yk , m
m

k ,m
)e
 jˆk ,m
dt
Log-MAP MUD Algorithm:
Setup

Place y and b into vectors:
Log-MAP MUD
ym   y1,m [1], , yK ,m [1], , y1,m [ L], , yK ,m [ L]
b m  b1,m [1], , bK ,m [1], , b1,m [ L], , bK ,m [ L]

Place the fading amplitudes into a vector:
cm 


P1,m [1] , , Pk ,m [1] , , P1,m [ L] , , PK ,m [ L]

Compute cross-correlation matrix for each BS:

Assuming rectangular pulse shaping.
 i  j ,m   j ,m

cos(i  j ,m   j ,m ),
if i  j  K

T
Gi(,mj )  
T  ( j ,m   i  j  K ,m )

cos(i  j  K ,m   j ,m ) if i  j  K

T
Log-MAP MUD Algorithm:
Execution
 (si )  ( si  si 1 )
S3
 ( si 1 )
Log-MAP MUD
S2
S1
S0
i=0
i=1
i=2
i=3
i=4
i=5
Jacobian Logarithm:
ln( e x  e y )  max( x, y)  ln( 1  e | y  x| )
Branch Metric:
K 1


 i ,m (b)  bi ci ,m 2 yi ,m  bi ci ,m  2 bi  j ci d ,mG (Km j), ( i ) 
j 1


i=6
Simulation Parameters

The uplink of a TDMA system was simulated.

Simulation

120 degree sectorized antennas.
3 cochannel interferers in the first tier.





Fully-interleaved Rayleigh flat-fading.
Perfect channel estimation assumed.
Each user is convolutionally encoded.



K=3 users.
M=3 base stations.
Constraint Length W = 3.
Rate r = 1/2.
Block size L=4,096 bits

64 by 64 bit block interleaver
Performance for Constant
C/I = 7dB
0
10
Conventional Reception
Macrodiverity using Conventional Receivers
joint MUD/FEC with macrodiversity, 1st iteration
joint MUD/FEC with macrodiversity, 2nd iteration
joint MUD/FEC with macrodiversity, 3rd iteration
joint MUD/FEC with macrodiversity, 4th iteration
-1
-2
10
Bit Error Rate (BER)
Simulation
10
-3
10
-4
10
-5
10
-6
10
0
2
4
6
8
E b/No in dB
10
12
14
Performance for Constant
Eb/No = 6dB
0
10
Conventional Reception
Macrodiverity using Conventional Receivers
joint MUD/FEC with macrodiversity, 1st iteration
joint MUD/FEC with macrodiversity, 2nd iteration
joint MUD/FEC with macrodiversity, 3rd iteration
joint MUD/FEC with macrodiversity, 4th iteration
-1
-2
10
Bit Error Rate (BER)
Simulation
10
-3
10
-4
10
-5
10
-6
10
0
2
4
6
8
10
C/I in dB
12
14
16
18
20
Conclusion and Future Work


MUD can improve the performance of TDMA system.
Performance can be further improved by:
Conclusions



This requires that the output of both the MUD’s and
FEC-decoders be in the form of log-likelihood ratios.


Combining the outputs of the base stations.
Performing iterative error correction and multiuser detection.
Log-MAP algorithm used for both MUD and FEC.
The study assumes perfect channel estimates.


The effect of channel estimation should be considered.
Decision directed estimation should be possible.

Output of each base station can assist estimation at the others.
Uncoded Performance for
Constant C/I
0
10

Conventional Receiver
MUD without diversity
MUD with equal gain combining

-1
Bit Error Rate
10

-2
10
-3
10
-4
10
0
5
10
15
Eb/No in dB
20
25
30
C/I = 7 dB
Performance improves with
MUD at one base station.
An additional performance
improvement obtained by
combining the outputs of the
three base stations.
Uncoded Performance for
Constant Eb/No

0
10
Conventional Receiver
MUD without diversity
MUD with equal gain combining

-1
Bit Error Rate
10
-2
10

-3
10
Performance as a function of C/I.
 Eb/No = 20 dB.
For conventional receiver,
performance is worse as C/I gets
smaller.
Performance of single-base
station MUD is invariant to C/I.


-4
10
-5
10
0
2
4
6
8
10
12
C/I in dB
14
16
18
20
Near-far resistant.
For macrodiversity combining,
performance improves as C/I gets
smaller.