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Sphere-constrained ML detection for channels with memory

Vikalo, Haris and Hassibi, Babak and Mitra, Urbashi (2003) Sphere-constrained ML detection for channels with memory. In: Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, 2004. Vol.1. IEEE , Piscataway, NJ, pp. 672-676. ISBN 0-7803-8104-1.

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The maximum-likelihood (ML) detection problem for channels with memory is investigated. The Viterbi algorithm (VA) provides an exact solution. Its computational complexity is linear in the length of the transmitted sequence but exponential in the channel memory length. Hence, the VA can be computationally inefficient when employed for detection on long channels. On the other hand, the sphere decoding (SD) algorithm also solves the ML detection problem exactly and has expected complexity which is polynomial (often cubic) in the length of the transmitted sequence over a wide range of signal-to-noise ratios (SNR). We combine the sphere-constrained search strategy of SD with the dynamic programming principles of the VA. The resulting algorithm has the worst-case complexity of the VA, but often significantly lower expected complexity.

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Additional Information:© 2003 IEEE. This work was supported in part by the NSF under grant no. CCR-0133818, by the Office of naval research under grant no. N00014-02-1-0578, and by Caltech's Lee Center for Advanced Networking.
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Office of Naval Research (ONR)N00014-02-1-0578
Caltech Lee Center for Advanced NetworkingUNSPECIFIED
Record Number:CaltechAUTHORS:20150211-073457103
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:54680
Deposited By: Shirley Slattery
Deposited On:12 Feb 2015 01:04
Last Modified:10 Nov 2021 20:36

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