On maximum-likelihood detection and the search for the closest lattice point

Maximum-likelihood (ML) decoding algorithms for Gaussian multiple-input multiple-output (MIMO) linear channels are considered. Linearity over the field of real numbers facilitates the design of ML decoders using number-theoretic tools for searching the closest lattice point. These decoders are colle...

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Bibliographic Details
Published in:IEEE transactions on information theory Vol. 49; no. 10; pp. 2389 - 2402
Main Authors: Damen, M.O., El Gamal, H., Caire, G.
Format: Journal Article
Language:English
Published: New York IEEE 01.10.2003
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Amplitude modulation
Communications networks
Computational complexity
Computational modeling
Decoders
Decoding
Detectors
Feedback
Joints
Lattices
Linearity
Maximum likelihood decoding
Maximum likelihood detection
MIMO
Output
Reduction
Searching
Strategy
ISSN:0018-9448, 1557-9654
Online Access:Get full text
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Summary:Maximum-likelihood (ML) decoding algorithms for Gaussian multiple-input multiple-output (MIMO) linear channels are considered. Linearity over the field of real numbers facilitates the design of ML decoders using number-theoretic tools for searching the closest lattice point. These decoders are collectively referred to as sphere decoders in the literature. In this paper, a fresh look at this class of decoding algorithms is taken. In particular, two novel algorithms are developed. The first algorithm is inspired by the Pohst enumeration strategy and is shown to offer a significant reduction in complexity compared to the Viterbo-Boutros sphere decoder. The connection between the proposed algorithm and the stack sequential decoding algorithm is then established. This connection is utilized to construct the second algorithm which can also be viewed as an application of the Schnorr-Euchner strategy to ML decoding. Aided with a detailed study of preprocessing algorithms, a variant of the second algorithm is developed and shown to offer significant reductions in the computational complexity compared to all previously proposed sphere decoders with a near-ML detection performance. This claim is supported by intuitive arguments and simulation results in many relevant scenarios.
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ISSN:0018-9448
1557-9654
DOI:10.1109/TIT.2003.817444