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Randomized lattice decoding : bridging the gap between lattice reduction and sphere decoding

Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.14/114586

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Title: Randomized lattice decoding : bridging the gap between lattice reduction and sphere decoding
Related: IEEE International Symposium on Information Theory (13 - 18 June 2010 : Austin, Texas)
Related: 2010 IEEE International Symposium on Information Theory : (ISIT) : Austin, TX 13-18 June, 2010, p.2263-2267
DOI: 10.1109/ISIT.2010.5513543
Publisher: Piscataway, N.J : IEEE
Date: 2010
Author/Creator: Liu, Shuiyin
Author/Creator: Ling, Cong
Author/Creator: Stehlé, Damien
Description: Sphere decoding achieves maximum-likelihood (ML) performance at the cost of exponential complexity; lattice reduction-aided decoding significantly reduces the decoding complexity, but exhibits a widening gap to ML performance as the dimension increases. To bridge the gap between them, this paper presents randomized lattice decoding based on Klein's randomized algorithm, which is a randomized version of Babai's nearest plane algorithm. The technical contribution of this paper is twofold: we analyze and optimize the performance of randomized lattice decoding resulting in reduced decoding complexity, and propose a very efficient implementation of random rounding. Simulation results demonstrate near-ML performance achieved by a moderate number of calls, when the dimension is not too large.
Description: 5 page(s)
Subject Keyword: decoding complexity
Subject Keyword: efficient implementation
Subject Keyword: information theory
Subject Keyword: maximum likelihood
Subject Keyword: spheres
Subject Keyword: decoding
Subject Keyword: exponential complexity
Subject Keyword: lattice decoding
Subject Keyword: lattice reduction
Subject Keyword: random rounding
Subject Keyword: randomized algorithms
Subject Keyword: simulation result
Subject Keyword: sphere decoding
Subject Keyword: technical contribution
Resource Type: conference paper
Organisation: Macquarie University. Dept. of Computing

Identifier: http://hdl.handle.net/1959.14/114586
Identifier: ISBN:9781424478910
Identifier: mq-rm-2010001572
Language: eng
Rights: Copyright 2010 IEEE. Reprinted from 2010 IEEE International Symposium on Information Theory : (ISIT) : Austin, TX 13-18 June, 2010. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Macquarie University’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
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