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An Optimal Soft-Output Multiuser Detection Algorithm and its Applications

An Optimal Soft-Output Multiuser Detection Algorithm and its Applications. Matthew C. Valenti Assistant Professor Comp. Sci. & Elect. Eng. West Virginia University Morgantown, WV U.S.A. mvalenti@wvu.edu. Outline of Talk. Turbo multiuser detection. Related work. System model.

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An Optimal Soft-Output Multiuser Detection Algorithm and its Applications

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  1. An Optimal Soft-OutputMultiuser Detection Algorithmand its Applications Matthew C. Valenti Assistant Professor Comp. Sci. & Elect. Eng. West Virginia University Morgantown, WV U.S.A. mvalenti@wvu.edu

  2. Outline of Talk • Turbo multiuser detection. • Related work. • System model. • The optimal SISO MUD algorithm • Applications of SISO MUD • Turbo multiuser detection. • Antenna arrays • Distributed multiuser detection. Introduction

  3. Turbo Multiuser Detection Time-varying FIR filter “multiuser interleaver” FEC Encoder #1 Channel interleaver #1 MAI Channel Model Parallel to Serial n(t) AWGN FEC Encoder #K Introduction interleaver #K Turbo MUD Extrinsic Info multiuser interleaver Bank of K SISO Decoders SISO MUD multiuser deinterleaver Estimated Data

  4. Some Developments in Turbo Multiuser Detection • Gialllorenzi and Wilson • 1996: Trans. Comm. • Hypertrellis approach. Not iterative. No interleaving. • Vojcic, Shama, Pickholtz • 1997: ISIT • Optimal Soft Output MAP. Asynchronous. Not iterative. • No noise whitening. • Reed, Schlegel, Alexander, Asenstorfer • 1997: Turbo Code Symposium, PIMRC. • Several Journal Papers (Trans. Comm., JSAC, ETT) • Early work considered synchronous, later asynchronous. • M. Moher • 1998: Trans. Comm. (synchronous), Comm. Letters. (asynchronous) • Based on cross entropy minimization. Introduction

  5. System Model encoder interleaver modulator bank of matched filters transmitter 1 receiver 1 asynchronous channel AWGN or complex Rayleigh fading bank of matched filters encoder interleaver modulator receiver M transmitter K

  6. Whitened Matched Filter Output • Matrix notation for output of matched filter at mth receiver • Cholesky decomposition • Whitened matched filter output colored noise crosscorrelations transmitted symbols (round-robin) channel gains (diagonal) Optimal SISO MUD white noise, variance = No/(2Es) lower triangular, only K diagonals

  7. Metric for Optimal SISO MUD • Trellis representation: • Noiseless Reconstruction of the signal: • Branch metric: • Now, just use MAP algorithm. Optimal SISO MUD constant ignore for LLR Squared Euclidian distance between received symbol and noiseless reconstruction of signal Term incorporating the extrinsic information Z

  8. Turbo MUD forDirect Sequence CDMA • CDMA: Code Division Multiple Access • The users are assigned distinct waveforms. • Spreading/signature sequences • All users transmit at same time/frequency. • Use a wide bandwidth signal • Processing gain Ns • Ratio of bandwidth after spreading to bandwidth before • MUD for CDMA • The resolvable MAI originates from the same cell. • Intracell interference. • MUD uses observations from only one base station. • M=1 case. Applications

  9. K = 5 users Spreading gain Ns = 7 Convolutional code: Kc = 3, r=1/2 Eb/No = 5 dB 1  K  9 Performance of Turbo-MUD for CDMA in AWGN

  10. K = 5 users Fully-interleaved fading Eb/No = 9 dB 1  K  9 Performance of Turbo-MUD for CDMA in Rayleigh Flat-fading

  11. Turbo MUD for TDMA • TDMA: Time Division Multiple Access • Users are assigned unique time slots • All users transmit at same frequency • All users have the same waveform, g(t) • TDMA can be considered a special case of CDMA, with gk(t) = g(t) for all cochannel k. • MUD for TDMA • Usually there is only one user per time-slot per cell. • The interference comes from nearby cells. • Intercell interference. • Observations from only one base station might not be sufficient. • Performance is improved by combining outputs from multiple base stations. Applications

  12. K = 3 users Convolutional code: Kc = 3, r=1/2 Observations at 1 base station Eb/No = 5 dB 1  K  9 Performance of Turbo-MUD for TDMA in AWGN

  13. K = 3 users Fully-interleaved fading Eb/No = 9 dB 1  K  9 Performance of Turbo-MUD for TDMA in Rayleigh Flat-Fading

  14. Antenna Arrays • Consider an antenna array with M elements. • In this case, M>1 • Each element has its own multiuser detector. • Can use the SISO MUD algorithm. • Antenna elements should be far enough apart that the signals are uncorrelated. Applications array element #1 Multiuser Detector #1 array element #M Multiuser Detector #M

  15. Distributed Multiuser Detection • Why must the elements of an antenna array be located at the same base station? • We could synthesize an antenna array by using the antennas of spatially separated base stations. • A benefit is now signals will be uncorrelated. Applications base station #1 Multiuser Detector #1 base station #M Multiuser Detector #M

  16. Alternative layout 120 degree sectorized antennas Located in 3 corners of cell Frequency reuse factor 3 Conventional layout Isotropic antennas in cell center Frequency reuse factor 7 F3 F4 F2 F1 F5 F7 F3 F6 F4 F2 F1 F5 F7 F6 Cellular Network Topology F3 F4 F2 F1 F5 F7 F6

  17. Performance of Distributed MUD • Eb/No = 20 dB • 1  K  9 • For conventional receiver: • Performance degrades quickly with increasing K. • Only small benefit to using observations from multiple BS. • With multiuser detection: • Performance degrades very slowly with increasing K. • Order of magnitude decrease in BER by using multiple observations. • Now multiple cochannel users per cell are allowed.

  18. Cooperative Decoding for the TDMA Uplink • Now consider the coded case. • The outputs of the MUD’s are summed and passed through a bank of decoders. • The SISO decoder outputs are fed back to the multiuser detectors to be used as a priori information. Applications Extrinsic Info Multiuser Detector #1 Bank of K SISO Channel Decoders Estimated Data Multiuser Detector #M

  19. Performance of Cooperative Decoding • K = 3 transmitters • Randomly placed in cell. • M = 3 receivers (BS’s) • Corners of cell • path loss ne = 3 • Fully-interleaved Rayleigh flat-fading • Convolutional code • Kc = 3, r = 1/2

  20. Performance of Cooperative Decoding • Eb/No = 5 dB • 1  K  9 • Randomly placed in cell. • M = 3 receivers • For conventional receiver: • Performance degrades quickly with increasing K. • Only small benefit to using observations from multiple BS. • With multiuser detection: • Performance degrades gracefully with increasing K. • No benefit after third iteration. • Could allow an increase in TDMA system capacity.

  21. Conclusion • An optimal SISO MUD algorithm has been derived. • Complexity is exponential in the number of users. • For many applications, the SISO MUD is too complex. • Traditional turbo-MUD for CDMA systems. • However, there are many applications where the SISO MUD is suitable. • Turbo-MUD for TDMA, hybrid CDMA/TDMA, WCDMA • SISO MUD can be used to achieve distributed detection. • Future work. • Comparison against suboptimal approaches. • Other applications of SISO MUD algorithms. Conclusion

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