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Low-Complexity Multiuser Detection Techniques for Enhanced Wireless Communication

This presentation by Rami Abdallah from the University of Illinois discusses various low-complexity multiuser detection (MUD) techniques to cancel or suppress interfering users from desired signals. It outlines the benefits of MUD, such as capacity improvement and reduced power control requirements, while addressing limitations like complexity and intercell interference. The presentation compares different MUD techniques, including linear detectors, successive interference cancellation (SIC), and sphere decoding, highlighting their trade-offs in complexity and performance. Techniques discussed can also apply to other detection problems, including MIMO.

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Low-Complexity Multiuser Detection Techniques for Enhanced Wireless Communication

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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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