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Wireless Communication Low Complexity Multiuser Detection

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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 - PowerPoint PPT Presentation

Text of Wireless Communication Low Complexity Multiuser Detection

Hollywood Project Weekly Status Report12/06/2007
Multiuser Detection (MUD): canceling or suppressing interfering users from the desired signals
Benefits:
Limitations:
Complexity
*
System Representation after MF:
Enables layered decoding
Multiple-Access Interference (MAI)
Independent User Decoding
Best near-far resistance
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
*
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
Parallel Interference Cancellation (PIC)
Every stage use previous estimates to subtract MAI for each user in parallel
Tradeoff between complexity and performance
Stage 1
Power Controlled
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
*
Noise whitening
SIC to cancel MAI among user (F is lower triangular)
r(t)
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
Preprocessing for SD
Triangularization in AWGN
QR Decomposition: a unitary matrix (Q) and an upper triangular matrix
Triangularization in MUD
Channel Normalization
Search Constraint: Radius or Best Candidates
*
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
d(z1,z2,z3)
Z1
C(z1)
C(z1,z2)
C(z1,z2,z3)
Z2
Z3
1
1
1
1
1
1
1
0
0
0
0
0
0
0
SD limits search space
Relaxation increases search space by dropping certain constraints so that the search is easier to implement
Unconstrained Relaxation (UR)
*
Semi-Definite Relaxation (SDR):
Drop rank 1 constraint on X with X still symmetric positive semi definite:
An efficient solution can be found in
*
Randomize to approximate xi from vi
*
Belief on the decision of user k at stage i
Update this belief by treating MAI as AWGN:
*
*
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?
R
z

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