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Wireless Communication Low Complexity Multiuser Detection

Wireless Communication Low Complexity Multiuser Detection. Rami Abdallah University of Illinois at Urbana Champaign 12/06/2007. Outline. Introduction. Multiuser Detection (MUD): canceling or suppressing interfering users from the desired signals Benefits: Capacity Improvement

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Wireless Communication Low Complexity Multiuser Detection

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  1. Wireless CommunicationLow Complexity Multiuser Detection Rami Abdallah University of Illinois at Urbana Champaign 12/06/2007

  2. Outline

  3. Introduction • Multiuser Detection (MUD): canceling or suppressing interfering users from the desired signals • Benefits: • Capacity Improvement • Reduced requirement for power control • Limitations: • Complexity • Intercell interference • Spreading – Coding tradeoff

  4. Problem Definition • Optimum Multiuser Detection • Search space exponential in number of users

  5. System Representation • Matched Filter (MF) • Received Signal for user k: • System Representation after MF: • Noise Whitening • Cholesky Decomposition to decorrelate noise • Enables layered decoding Multiple-Access Interference (MAI)

  6. Linear Detectors (1) • Decorrelating Detector • Solve for z by inverting R • Independent User Decoding • Best near-far resistance • Noise enhancement • Optimal Linear Detector (MMSE) • Trade-off between MAI elimination and noise enhancement

  7. Linear Detectors (2) • Polynomial Expansion (PE) Detector : • Weighted sum of MF output (R) • Weights (W) chosen depending on a performance criterion and can be adaptively updated • Can approximate decorrelating and MMSE detector (Cayley-Hamilton Theorem) • Regular architecture avoiding Matrix inversion

  8. Interference Cancellation • Successive Interference Cancellation (SIC) • Order users according to descending power • Start detection with the highest power first and subtract its effect from the received signal • Successive users benefits more for MAI cancellation • Problems: • Latency • Decision error propagation

  9. Interference Cancellation (2) • Parallel Interference Cancellation (PIC) • Every stage use previous estimates to subtract MAI for each user in parallel • Tradeoff between complexity and performance

  10. Performance Comparison Power Controlled • PIC superior over SIC in well-power controlled environment

  11. Variations of PIC • Multistage decision feed-back detector: • In each stage use the already detected bits to improve detection of remaining bits in the same stage • Partial interference cancellation • Decision is based on • Partially cancel MAI with the amount being cancelled increasing with each stage

  12. Decision Feedback MUD • Decision feed-back detector: • User ordering in terms of descending power • Noise whitening • SIC to cancel MAI among user (F is lower triangular)

  13. Sphere (lattice) Decoder • Sphere Decoders (SD) in AWGN Channel • ML: Search over all • SD: Restrict search within a sphere of center s and radius R • Complexity tradeoff in terms of choosing radius R • H: channel, n : AWGN

  14. Preprocessing for SD • Triangularization in AWGN • QR Decomposition: a unitary matrix (Q) and an upper triangular matrix • Triangularization in MUD • Noise Whitening Still AWGN with equal variance New received vector

  15. Sphere Decoders • Layered/ Tree-based Decoding • Partial Euclidean Distance Accumulations by taking advantage of channel triangularization • Search Constraint: Radius or Best Candidates

  16. Constrained SD • Depth First SD • Search the tree in downward and upward manner • Update the search radius after each pass • Breadth First (K-best SD) • Search in downward direction only • K best candidates are retained at each level in the tree

  17. Performance Comparison • 1000X reduction in complexity

  18. Relaxations and Heuristics • SD limits search space • Relaxation increases search space by dropping certain constraints so that the search is easier to implement • Unconstrained Relaxation (UR) • Remove constraint on Alphabet • Penalized UR: Compare to MF, Decorrelator, MMSE

  19. Semi-Definite Relaxation • Problem Setup: • Semi-Definite Relaxation (SDR): • Drop rank 1 constraint on X with X still symmetric positive semi definite: • An efficient solution can be found in

  20. Semi-definite Relaxation (2) • Approximate Boolean solution by randomization • Randomize to approximate xi from vi

  21. SDR for MUD SNR3=11dB

  22. Probabilistic Data Association • Problem Setup: • PDA • Order users in decreasing power • Belief on the decision of user k at stage i • Update this belief by treating MAI as AWGN: • Stop when belief converges, Decide by comparing p to 0.5

  23. Performance Comparison Average BER with K=29 with gold codes

  24. Conclusions • Multiuser Detection (MUD): canceling or suppressing interfering users from the desired signals • Different techniques exist that trade-off complexity with performance • Detection techniques can be applied to other detection problems (ex. MIMO) • Viterbi Algorithm can be applied to MUD, How would low complexity “Viterbi algorithm” behave under MUD?

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