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Optimal Training Design for Channel Estimation in Decode-and-Forward Relay Networks With Individual and Total Power Constraints

Gao, Feifei and Cui, Tao and Nallanathan, Arumugam (2008) Optimal Training Design for Channel Estimation in Decode-and-Forward Relay Networks With Individual and Total Power Constraints. IEEE Transactions on Signal Processing, 56 (12). pp. 5937-5949. ISSN 1053-587X. doi:10.1109/TSP.2008.2005084.

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In this paper, we study the channel estimation and the optimal training design for relay networks that operate under the decode-and-forward (DF) strategy with the knowledge of the interference covariance. In addition to the total power constraint on all the relays, we introduce individual power constraint for each relay, which reflects the practical scenario where all relays are separated from one another. Considering the individual power constraint for the relay networks is the major difference from that in the traditional point-to-point communication systems where only a total power constraint exists for all colocated antennas. Two types of channel estimation are involved: maximum likelihood (ML) and minimum mean square error (MMSE). For ML channel estimation, the channels are assumed as deterministic and the optimal training results from an efficient multilevel waterfilling type solution that is derived from the majorization theory. For MMSE channel estimation, however, the second-order statistics of the channels are assumed known and the general optimization problem turns out to be nonconvex. We instead consider three special yet reasonable scenarios. The problem in the first scenario is convex and could be efficiently solved by state-of-the-art optimization tools. Closed-form waterfilling type solutions are found in the remaining two scenarios, of which the first one has an interesting physical interpretation as pouring water into caves.

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Additional Information:© Copyright 2008 IEEE. Reprinted with permission. Manuscript received October 09, 2007; revised April 16, 2008. First published August 26, 2008; current version published November 19, 2008. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Zhengyuan (Daniel) Xu.
Subject Keywords:Cave-filling; channel estimation; decode-and-forward; majorization theory; maximum likelihood; minimum mean square error; optimal training; relay networks; waterfilling
Issue or Number:12
Record Number:CaltechAUTHORS:AOieeetsp08
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:12861
Deposited By: Archive Administrator
Deposited On:07 Jan 2009 20:36
Last Modified:08 Nov 2021 22:33

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