Combined Multiuser Detection and Channel Decoding with Receiver Diversity

1. Combined Multiuser Detection and Channel Decodingwith Receiver Diversity IEEE GLOBECOM Communications Theory Mini-Conference Sydney, Australia November 10, 1998 Matthew C. Valenti and Brian D. Woerner Mobile and Portable Radio Research Group Virginia Tech Blacksburg, Virginia

2. Outline of Talk • 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. Outline

3. Multiuser Detection for the TDMA Uplink • For CDMA systems: • Resolvable interference comes from within the same cell. • Each cochannel user has a distinct spreading code. • Large number of (weak) cochannel interferers. • For TDMA systems: • 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. MUD for TDMA

4. Macrodiversity Combining for the TDMA Uplink • 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. BS 1 MS 1 BS 3 Macrodiversity MS 3 MS 2 BS 2

5. Macrodiversity Combiner • Each of M base stations has a multiuser detector. • Each MUD produces a log-likelihood ratio of the code bits. • The LLR’s are added together prior to the final decision. Macrodiversity Multiuser Estimator #1 Multiuser Estimator #M

6. Turbo Multiuser Detection • 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

7. Turbo MUD: 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

8. Multiuser Estimator #1 Bank of K SISO Channel Decoders Multiuser Estimator #M 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. Turbo MUD w/ Macrodiversity

9. The Log-MAP Algorithm • The Viterbi Algorithm can be used to implement: • 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. • Bahl, Cocke, Jelinek, Raviv, 1974. (BCJR Algorithm) • The Log-MAP algorithm is performed in the Log domain, • Robertson, Hoeher, Villebrun, 1997. • More stable, less complex than BCJR Algorithm. • We use Log-MAP for both MUD and FEC. Log-MAP Algorithm

10. MAI Channel Model • Received signal at base station m: • 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: Log-MAP MUD

11. Log-MAP MUD Algorithm:Setup • Place y and b into vectors: • Place the fading amplitudes into a vector: • Compute cross-correlation matrix for each BS: • Assuming rectangular pulse shaping. Log-MAP MUD

12. Log-MAP MUD Algorithm:Execution S3 S2 S1 Log-MAP MUD S0 i = 0 i = 1 i = 2 i = 3 i = 4 i = 5 i = 6 Jacobian Logarithm: Branch Metric:

13. Simulation Parameters • The uplink of a TDMA system was simulated. • 120 degree sectorized antennas. • 3 cochannel interferers in the first tier. • K=3 users. • M=3 base stations. • Fully-interleaved Rayleigh flat-fading. • Perfect channel estimation assumed. • Each user is convolutionally encoded. • Constraint Length W = 3. • Rate r = 1/2. • Block size L=4,096 bits • 64 by 64 bit block interleaver Simulation

14. Performance for Constant C/I = 7dB Simulation

15. Performance for Constant Eb/No = 6dB Simulation

16. Conclusion and Future Work • MUD can improve the performance of TDMA system. • Performance can be further improved by: • Combining the outputs of the base stations. • Performing iterative error correction and multiuser detection. • This requires that the output of both the MUD’s and FEC-decoders be in the form of log-likelihood ratios. • 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. Conclusions

17. Uncoded Performance for Constant C/I • 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.

18. Uncoded Performance for Constant Eb/No • 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. • Near-far resistant. • For macrodiversity combining, performance improves as C/I gets smaller.