Introduction to Space-Time Wireless Communications
Introduction to Space-Time Wireless Communications
Reduced complexity sphere decoding via a reordered lattice representation
IEEE Transactions on Communications
On the complexity of sphere decoding in digital communications
IEEE Transactions on Signal Processing
On the sphere-decoding algorithm I. Expected complexity
IEEE Transactions on Signal Processing - Part I
Closest point search in lattices
IEEE Transactions on Information Theory
Diversity and multiplexing: a fundamental tradeoff in multiple-antenna channels
IEEE Transactions on Information Theory
On maximum-likelihood detection and the search for the closest lattice point
IEEE Transactions on Information Theory
Soft-output sphere decoding: algorithms and VLSI implementation
IEEE Journal on Selected Areas in Communications
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In multiple-input multiple-output (MIMO) systems, sphere decoding (SD) can achieve a performance equivalent to a full-search maximum likelihood decoding, with reduced complexity. Several researchers reported techniques that reduce the complexity of SD further. In this paper, a new technique that decreases the computational complexity of SD substantially, without sacrificing performance, is introduced. The reduction is accomplished by deconstructing the decoding metric to decrease the number of computations and by exploiting the structure of a lattice representation. Furthermore, an application of SD employing a proposed smart implementation with very low computational complexity is introduced. This application calculates the soft bit metrics of a bit-interleaved convolutional-coded MIMO system in an efficient manner. On the basis of the reduced complexity SD, the proposed smart implementation employs the initial radius acquired by zero-forcing decision feedback equalization, which ensures no empty spheres. Other than that, a technique of a particular data structure is also incorporated to efficiently reduce the number of executions carried out by SD. Simulation results show that these approaches achieve substantial gains in terms of the computational complexity for both uncoded and coded MIMO systems. Copyright © 2011 John Wiley & Sons, Ltd. (In this paper, the term “spatial multiplexing” is used to describe the number of spatial subchannels, as in **. Note that the term is different from “spatial multiplexing gain” defined in *.)