1 / 21

Optimal Multiuser Detection Algorithm: Theory and Applications

Explore the optimal Soft-Output Multiuser Detection (SISO.MUD) algorithm, its applications in Turbo MUD, CDMA, TDMA, and Antenna Arrays.

dwooding
Download Presentation

Optimal Multiuser Detection Algorithm: Theory and Applications

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related