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Title: The value of feedback in decentralized detection
Authors: Tay, Wee Peng
Keywords: Decentralized Detection
Error Exponent
DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2012
Source: Tay, W. P. (2012). The value of feedback in decentralized detection. IEEE Transactions on Information Theory, 58(12), 7226-7239. doi:10.1109/TIT.2012.2211331
Series/Report no.: IEEE Transactions on Information Theory
Abstract: We consider the decentralized binary hypothesis testing problem in networks with feedback, where some or all of the sensors have access to compressed summaries of other sensors' observations. We study certain two-message feedback architectures, in which every sensor sends two messages to a fusion center, with the second message based on full or partial knowledge of the first messages of the other sensors. We also study one-message feedback architectures, in which each sensor sends one message to a fusion center, with a group of sensors having full or partial knowledge of the messages from the sensors not in that group. Under either a Neyman-Pearson or a Bayesian formulation, we show that the asymptotically optimal (in the limit of a large number of sensors) detection performance (as quantified by error exponents) does not benefit from the feedback messages, if the fusion center remembers all sensor messages. However, feedback can improve the Bayesian detection performance in the one-message feedback architecture if the fusion center has limited memory; for that case, we determine the corresponding optimal error exponents.
ISSN: 0018-9448
DOI: 10.1109/TIT.2012.2211331
Schools: School of Electrical and Electronic Engineering 
Rights: © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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