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. Introduction

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! • Don’t 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. Introduction

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. Introduction

5. System Model • Received Signal: • For TDMA: • Matched Filter Output: System Model

6. Optimal Multiuser Detection • Place y and b into vectors: • Compute cross-correlation matrix: • For the TDMA case the above reduces to: MUD

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. MUD

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. MUD

9. Proposed System Architecture Interleaver • Each user interleaves its coded bits prior to transmission. • Initialize p(bi) = 1/2 SISO Multiuser Detector Deinterleaver SISO Channel Decoders Matched Filters Deinterleaver System Model Channel Estimator

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. Example

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. Example

12. Simulation Results: Existing Methods -1 10 • At BER=10-3 • 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 Results:New 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. Conclusion

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. Conclusion

16. Future Work • Combine MUD, decoding, and base station diversity. MUD at B.S. #1 Conclusion Bank of K SISO Channel Decoders Maximal Ratio Combining MUD at B.S. #M

17. Simulation Results:Diversity 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. • K=3 r=1/2 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