West Virginia University 7/8/99 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 Introduce yourself. Funded by the Bradley Fellowship and the Office of Naval Research. Iterative Detection and Decoding for Wireless Communications

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

Turbo Multiuser Detection Time-varying FIR filter “multiuser interleaver” FEC Encoder #1 interleaver #1 Channel 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

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

complex Rayleigh fading 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

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

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

Turbo MUD for Direct 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

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

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

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

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

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

Cellular Network Topology F2 F1 F3 F4 F5 F6 F7 F3 F2 F4 F1 F7 F5 F2 F1 F3 F4 F5 F6 F7 F6 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

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.

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

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

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.

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