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## Multiuser Detection with Base Station Diversity

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**Multiuser Detection with Base Station Diversity**IEEE International Conference on Universal Personal Communications Florence, Italy October 9, 1998 Matthew C. Valenti and Brian D. Woerner Mobile and Portable Radio Research Group Virginia Tech Blacksburg, Virginia**Outline of Talk**• Multiuser detection for TDMA systems. • Macrodiversity combining for multiuser detected TDMA. • The Log-MAP MUD algorithm. • Simulation results for fading channels. • Extensions to coded systems. Outline**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 cochannel interferers. • For TDMA systems: • Cochannel interference comes from other cells. • Cochannel users do not have distinct spreading codes. • Small number of 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**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**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: System Model**Proposed System**• 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. System Model Multiuser Estimator #1 Multiuser Estimator #M**The Log-MAP MUD Algorithm**• Optimal MUD uses the Viterbi algorithm • Verdu, 1984 • This algorithm produces hard bit decisions. • The proposed system requires a multiuser estimation algorithm that produces LLR’s. • The symbol-by-symbol MAP algorithm can be used. • Bahl, Cocke, Jelinek, Raviv, 1974. • The Log-MAP algorithm is performed in the Log domain, • Robertson, Hoeher, Villebrun, 1997. • The complexity of Log-MAP MUD is O(2K). • This is too complex for CDMA. • However for TDMA, K is small, and this is reasonable. Log-MAP MUD**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**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:**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. • Assume perfect channel estimation. • No error correction coding. Simulation**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.**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.**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. Coded Systems**Performance for Constant C/I**• TDMA uplink. • K=3 mobiles. • M=3 base stations. • C/I = 7 dB • Convolutionally coded. • Constraint length 3. • Code rate 1/2. • Log-MAP algorithm. • MUD. • Channel decoder. • Iterative processing. • LLR from decoder fed back to MUD’s.**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. • This requires that the output of the MUD be in the form of a log-likelihood ratio. • Log-MAP MUD algorithm. • FEC-decoders can provide a priori information to the base stations (see paper in Globecom CTMC). • 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