Iterative Multiuser Detection for Convolutionally Coded Asynchronous DS-CDMA

1. Iterative Multiuser Detection for Convolutionally Coded Asynchronous DS-CDMA 9th IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications Boston, MA September 9, 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. • Implemented with Viterbi algorithm, complexity O(2K). • Optimal MUD is too complex for large K. • Suboptimal approximations • Decorrelator, MMSE,DFE, PIC, SIC, etc. • Most studies on MUD concentrate on the uncoded performance. • Here we consider the effects of coding. • We propose a receiver structure that approximates joint MUD and FEC-decoding. • The algorithm allows for asynchronous users and fading. Introduction

3. MUD for Coded DS-CDMA • Practical DS-CDMA systems use error correction coding (convolutional codes). • Soft-decision decoding outperforms hard-decision decoding (by about 2.5dB). • However, the optimal MUD passes hard-decisions to the channel decoder! • Therefore it is possible for the coded performance of a system with MUD to be worse than the coded performance without the MUD. • If MUD and FEC are to be used,the interface should be improved. • The decoder for turbo codes gives insight on how to improve this interface. • Use soft-decisions and feedback. Introduction

4. Relation to Other Work • T. Giallorenzi and S. Wilson • Optimal joint MUD/FEC-decoding • Trans. Comm. Aug. 1996 • Uses a “super-trellis”. • High complexity: O(2WK) • Suboptimal approaches. • Trans Comm. Sept. 1996 • Separate MUD and Channel decoding. • Soft values passed from MUD to channel decoder. • No feedback used. • See also P. Hoeher’s paper at ICUPC ‘93. Background

5. Relation to Other Work • M. Reed, C. Schlegel, et al • Feedback from FEC-decoder to MUD • Similar to the decoder for turbo codes. • Synchronous DS-CDMA • “One-shot” detector. • Convolutional codes • Turbo Code Symp ‘97, ICUPC ‘97 • Turbo codes • PIMRC ‘97 • Close to single-user bound for K=5 users and spreading gain of N=7. • AWGN channel Background

6. Relation to Other Work • M. Moher • Feedback from FEC-decoder to MUD. • Multiuser systems with high signal correlation. • FDMA with overlapping signals. • Random interleaving. • Synchronous systems • Trans. Comm., July 1998 • Asynchronous systems • Comm. Letters, Aug. 1998 • Close to single user bound for K=5,10 and =0.6,0.75 • K-symmetric channel. • AWGN Background

7. Turbo Codes and Iterative Decoding • A turbo code is the parallel concatenation of two convolutional codes. • An interleaver separates the code. • Recursive Systematic Convolutional (RSC) codes are typically used. Turbo Processing RSC Encoder #1 Data Output interleaver RSC Encoder #1

8. Turbo Decoding • A turbo decoder consists of two elementary decoders that work cooperatively. • Soft-in soft-out (SISO) decoders. • Implemented with Log-MAP algorithm • Feedback. • Each decoder produces a posteriori information, which is used as a priori information by the other decoder. • Iterative Turbo Processing A priori probability A priori probability SISO Decoder #1 SISO Decoder #2 Received Data Estimated Data

9. Serial Concatenated Codes • The turbo decoder can also be used to decode serially concatenated codes. • Typically two convolutional codes. n(t) AWGN Inner Convolutional Encoder Outer Convolutional Encoder Turbo Processing Data interleaver Turbo Decoder interleaver APP Outer SISO Decoder Inner SISO Decoder Estimated Data deinterleaver

10. Turbo Equalization • The “inner code” of a serial concatenation could be an Intersymbol Interference (ISI) channel. • ISI channel can be interpreted as a rate 1 code defined over the field of real numbers. n(t) AWGN (Outer) Convolutional Encoder Turbo Processing ISI Channel Data interleaver Turbo Equalizer interleaver APP (Outer) SISO Decoder SISO Equalizer Estimated Data deinterleaver

11. Turbo Multiuser Detection • The “inner code” of a serial concatenation could be a MAI channel. • MAI channel can be thought of as a time varying ISI channel. • MAI channel is a rate 1 code with time-varying coeficients over the field of real numbers. • The input to the MAI channel consists of the encoded and interleaved sequences of all K users. Turbo MUD

12. System Diagram “multiuser interleaver” Convolutional Encoder #1 interleaver #1 MAI Channel MUX n(t) AWGN Convolutional Encoder #K interleaver #K Turbo MUD Turbo MUD multiuser interleaver APP Bank of K SISO Decoders SISO MUD multiuser deinterleaver Estimated Data

13. MAI Channel Model • Received Signal: • Where: • ak is the signature waveform of user k. • k is a random delay (i.e. asynchronous) of user k. • Pk[i] is received power of user k’s ith bit (fading ampltiude). • Matched Filter Output: System Model

14. Optimal Multiuser Detection Algorithm: Setup • Place y and b into vectors: • Place the fading amplitudes into a vector: • Compute cross-correlation matrix: MUD

15. Optimal MUD: Execution • Run Viterbi algorithm with branch metric: • where • Note that most derivations of the optimal MUD drop the p(b) term. • Here we keep it. • The channel decoder will provide this value. • The algorithm produces hard bit decisions. • Not suitable for soft-decision channel decoding. MUD

16. Soft-Output MUD • Several algorithms can be used to produce soft-outputs (preferably log-likelihood ratio). • 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. • Linear: decorrelator, MMSE. • Subtractive (nonlinear): DFE, SIC, PIC. MUD

17. Simulation Parameters • K=5 users • Power controlled (same average power). • N=7 (processing gain), code-on-pulse. • Random spreading codes. • Convolutional Code • Constraint length 3. • Rate 1/2. • Interleaving • 24 by 22 block interleaver (L=528). • Log-MAP decoding. • Both MUD and channel decoder. • 3 iterations. Example

18. Simulation Results: AWGN Channel • After the second iteration, performance is close to single-user bound for BER greater than 10-4. • For BER less than 10-4, the curves diverge. • This behavior is similar to the “BER floor” in turbo codes. • Only a slight incremental gain by performing a third iteration. • The extra processing for the third iteration is not worth it.

19. Simulation Results:Rayleigh Flat-Fading Channel • Fully-interleaved Rayleigh flat-fading. • i.e. fades are independent from symbol to symbol. • After second iteration, performance is close to the single-user bound. • The curves do not diverge as they did for AWGN. • Why? • The instantaneous received power is different for the different users. • Therefore the MUD has one more parameter it can use to separate signals.

20. Conclusion • A strategy for iterative MUD/FEC-decoding is proposed. • Based on the concept of turbo processing. • Similar to other researchers’ work, but the algorithm is generalized to allow: • independently faded signals • code and bit asynchronism. • Proposed strategy was illustrated by simulation example. • Significant performance gain by performing 2 iterations. • When signals are independently faded, the algorithm exploits the differences in instantaneous signal power. Conclusion

21. Future Work • The study assumes perfect channel estimates. • The effect of channel estimation should be considered. • The estimator could be incorporated into the feedback loop. • The proposed strategy is still very complex • O(2W+2K) per iteration. • Future work should consider the use of reduced complexity multiuser detectors. • This structure could also be used for TDMA systems. • TDMA: only a few strong interferers, small K. • Highly correlated signals, can take advantage of this system. • Can use observations from multiple base stations. • See our work at VTC, ICUPC, and Globecom CTMC. Conclusion