New Iterative Detector of MIMO Transmission Using Sparse Decomposition

This paper addresses the problem of decoding in large-scale multiple-input-multiple-output (MIMO) systems. In this case, the optimal maximum-likelihood (ML) detector becomes impractical due to an exponential increase in the complexity with the signal and the constellation dimensions. This paper intr...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2015-08, Vol.64 (8), p.3458-3464
Main Authors: Fadlallah, Yasser, Aissa-El-Bey, Abdeldjalil, Amis, Karine, Pastor, Dominique, Pyndiah, Ramesh
Format: Article
Language:English
Subjects:
Bit error rate
Complexity
Computational complexity
Constellations
Decoding
Detectors
Engineering Sciences
Iterative methods
MIMO
Optimization
Polynomials
Signal and Image processing
Signal to noise ratio
Strategy
Vectors
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Summary:This paper addresses the problem of decoding in large-scale multiple-input-multiple-output (MIMO) systems. In this case, the optimal maximum-likelihood (ML) detector becomes impractical due to an exponential increase in the complexity with the signal and the constellation dimensions. This paper introduces an iterative decoding strategy with a tolerable complexity order. We consider a MIMO system with finite constellation and model it as a system with sparse signal sources. We propose an ML relaxed detector that minimizes the Euclidean distance with the received signal while preserving a constant \ell_{1} -norm of the decoded signal. We also show that the detection problem is equivalent to a convex optimization problem, which is solvable in polynomial time. Two applications are proposed, and simulation results illustrate the efficiency of the proposed detector.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2014.2360687