On Maximum-Likelihood Detection and the Search for the Closest Lattice Point

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On Maximum-Likelihood Detection and the Search for the Closest Lattice Point (2003)

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by Mohamed Oussama Damen , Hesham El Gamal , Giuseppe Caire

Venue:	IEEE TRANS. INFORM. THEORY
Citations:	272 - 9 self

BibTeX

@ARTICLE{Damen03onmaximum-likelihood,
    author = {Mohamed Oussama Damen and Hesham El Gamal and Giuseppe Caire},
    title = {On Maximum-Likelihood Detection and the Search for the Closest Lattice Point},
    journal = {IEEE TRANS. INFORM. THEORY},
    year = {2003},
    volume = {49},
    number = {10},
    pages = {2389--2402}
}

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Abstract

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.

Keyphrases

maximum-likelihood detection closest lattice point sphere decoder significant reduction second algorithm many relevant scenario real number ml decoder gaussian multiple-input multiple-output number-theoretic tool near-ml detection performance pohst enumeration strategy first algorithm lattice point stack sequential decoding algorithm intuitive argument detailed study viterbo boutros novel algorithm computational complexity schnorr euchner strategy simulation result fresh look

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