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Efficient joint maximum-likelihood channel estimation and signal detection

Vikalo, Haris and Hassibi, Babak and Stoica, Petre (2006) Efficient joint maximum-likelihood channel estimation and signal detection. IEEE Transactions on Wireless Communications, 5 (7). pp. 1838-1845. ISSN 1536-1276.

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In wireless communication systems, channel state information is often assumed to be available at the receiver. Traditionally, a training sequence is used to obtain the estimate of the channel. Alternatively, the channel can be identified using known properties of the transmitted signal. However, the computational effort required to find the joint ML solution to the symbol detection and channel estimation problem increases exponentially with the dimension of the problem. To significantly reduce this computational effort, we formulate the joint ML estimation and detection as an integer least-squares problem, and show that for a wide range of signal-to-noise ratios (SNR) and problem dimensions it can be solved via sphere decoding with expected complexity comparable to the complexity of heuristic techniques.

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Additional Information:© Copyright 2006 IEEE. Reprinted with permission. Manuscript received August 23, 2004; revised March 30, 2005; accepted May 17, 2005. [Posted online: 2006-08-14] The associate editor coordinating the review of this paper and approving it for publication was C. Xiao. 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, by Caltech’s Lee Center for Advanced Networking, and by the Swedish Science Council (VR).
Subject Keywords:Integer least-squares problem, sphere decoding, wireless communications, multiple-antenna systems, expected complexity, joint detection and estimation
Issue or Number:7
Record Number:CaltechAUTHORS:VIKieeetwc06
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:5761
Deposited By: Archive Administrator
Deposited On:01 Nov 2006
Last Modified:02 Oct 2019 23:26

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