Combined Multiuser Reception and Channel Decoding for TDMA Cellular Systems

1
Combined Multiuser Receptionand Channel
Decodingfor TDMA Cellular Systems

• 48th Annual Vehicular Technology Conference
• Ottawa, Canada May 21, 1998
• Matthew Valenti and Brian D. Woerner
• Mobile and Portable Radio Research Group
• Virginia Tech
• Blacksburg, Virginia

2
Introduction

• Performance of multiple access systems can be
  improved by multiuser detection (MUD).
• Verdu, Trans. Info. Theory 86.
• Viterbi algorithm, complexity O(2K).
• MUD for CDMA systems.
• Jointly detect signals from the same cell.
• Optimal MUD is too complex for large K.
• MUD for TDMA systems.
• Jointly detect signal from within the cell plus
  one or two strong interferers from other cells.

3
MUD for Coded TDMA

• TDMA systems use error correction coding.
• Soft-decision decoding outperforms hard-decision
  decoding (2-2.5dB).
• However, the optimal MUD passes hard-decisions to
  the channel decoder!
• Dont use optimal MUD if loss due to
  hard-decision decoding is greater than gain due
  to multiuser detection.
• Alternatively, the interface between MUD and
  channel decoder could be improved.

4
Outline of Talk

• System Model.
• bit asynchronous.
• Generalized for both TDMA and CDMA.
• MUD for TDMA.
• Proposed Receiver Architecture.
• Turbo processing.
• Simulation results
• RSC coded system.
• 1 strong interferer.
• SOVA decoders.

5
System Model

• Received Signal
• For TDMA
• Matched Filter Output

6
Optimal Multiuser Detection

• Place y and b into vectors
• Compute cross-correlation matrix
• For the TDMA case the above reduces to

7
Optimal MUD (Continued)

• Run Viterbi algorithm with branch metric
• where
• Note that the p(b) term is usually dropped.
• The channel decoder will provide this value.
• The algorithm produces hard bit decisions.
• Not suitable for soft-decision channel decoding.

8
Soft-Output MUD

• Several algorithms can be used to produce
  soft-output.
• Trellis-based.
• MAP algorithm
• Log-MAP, Robertson et al, ICC 95
• OSOME, Hafeez Stark, VTC 97
• SOVA algorithm
• Hagenauer Hoeher, Globecom 89
• Non-trellis-based.
• Suboptimal, reduced complexity.

9
Proposed System Architecture
Interleaver
SISO Multiuser Detector
Deinterleaver
SISO Channel Decoders
Matched Filters
Deinterleaver
Channel Estimator

• Each user interleaves its coded bits prior to
  transmission.
• Initialize p(bi) 1/2

10
Simulation Parameters

• 2 users
• Desired user
• 1 co-channel interferer with 3 dB less power.
• Recursive Systematic Convolutional codes
• Constraint length 3.
• Rate 1/2.
• SOVA decoding.
• Both MUD and Channel decoder.
• Normalized outputs, Papke et al, ICC 96.

11
Simulation Details

• Conservative approach taken
• Only the desired user is decoded.
• No channel decoder for interferer.
• Only the APP of the systematic bits of the
  desired user is fed back to the MUD.
• The APP for the parity bits are not computed or
  used.

12
Simulation Results Existing Methods
-1
10

• At BER10-3
• MUD gain is 4.7 dB.
• Coding gain is 6.7 dB.
• Gain using hard output MUD and coding, 4.6 dB.
• Therefore it does not make sense to use
  (hard-outut) MUD and channel coding.

-2
10
-3
10
BER
-4
10
-5
10
matched filter, uncoded
multiuser detector (MUD), uncoded
MUD, hard-decision decoding
matched filter, soft-decision decoding
-6
10
4
6
8
10
12
14
16
18
E
/N
in dB
b
o
13
Simulation ResultsNew Method
-2
10

• The proposed iterative MUD / channel decoding
  strategy is used.
• At BER 10-5
• After 2 iterations, proposed method shows .4 dB
  improvement over channel decoding alone.
• After 3 iterations, the additional gain is .6 dB.
• No measurable gain for more than 3 iterations.

-3
10
-4
BER
10
-5
10
matched filter, soft-decision decoding
combined MUD/decoding, 2 iterations
combined MUD/decoding, 3 iterations
-6
10
9
9.5
10
10.5
11
11.5
12
12.5
13
13.5
14
E
/N
in dB
b
o
14
Conclusion

• Optimal MUD can be used for TDMA.
• However, if channel coding is used then the
  interface between MUD and decoder is critical.
• A strategy for iterative MUD/channel decoding is
  proposed.
• Based on the concept of turbo processing.
• Proposed strategy was illustrated by simulation
  example.
• Modest gain by using proposed strategy over
  channel coding alone.

15
Future Work

• More aggressive use of soft-information.
• Use parity information for RSC codes, or use
  conventional convolutional coding.
• Decode the interfering user.
• Share information among base stations.
• Decode each user at the closest base station.
• Send the results to all the other base stations.
• Use MAP algorithm instead of SOVA.
• Fading, channel estimation, and equalization.

16
Future Work

• Combine MUD, decoding, and base station diversity.

MUD at B.S. 1
Bank of K SISO Channel Decoders
Maximal Ratio Combining
MUD at B.S. M
17
Simulation ResultsDiversity Combining
0

• 2 users and 2 base station.
• At each B.S. closer user is 3 dB stronger than
  more distant one.
• Rayleigh fading channel.
• log-MAP decoder and MUD.
• K3 r1/2 conventional convolutional code.
• 4 dB gain after 1 iteration
• 6 dB after 2 iterations.

10
MUD and decoding only
MUD/decoding/diversity One iteration
MUD/decoding/diversity Two iterations
-1
10
-2
10
BER
-3
10
-4
10
-5
10
0
2
4
6
8
10
12
14
E
/N
in dB
b
o