Multiuser%20Detection%20with%20Base%20Station%20Diversity

1
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

2
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.

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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.

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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
MS 3
MS 2
BS 2
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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,mi is power at receiver m of user ks ith
  bit.
• Matched filter output for user k at base station
  m

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Proposed System

• Each of M base stations has a multiuser detector.
• Each MUD produces a log-likelihood ratio of the
  code bits.
• The LLRs are added together prior to the final
  decision.

Multiuser Estimator 1
Multiuser Estimator M
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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 LLRs.
• 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.

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Log-MAP MUD AlgorithmSetup

• Place y and b into vectors
• Place the fading amplitudes into a vector
• Compute cross-correlation matrix for each BS
• Assuming rectangular pulse shaping.

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Log-MAP MUD AlgorithmExecution
S3
S2
S1
S0
i 0
i 6
i 3
i 2
i 1
i 4
i 5
Jacobian Logarithm
Branch Metric
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Simulation Parameters

• The uplink of a TDMA system was simulated.
• 120 degree sectorized antennas.
• 3 cochannel interferers in the first tier
• K3 users
• M3 base stations.
• Fully-interleaved Rayleigh flat-fading.
• Assume perfect channel estimation.
• No error correction coding.

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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.

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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.

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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.

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Performance for Constant C/I

• TDMA uplink.
• K3 mobiles.
• M3 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 MUDs.

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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.