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このアイテムの引用には次の識別子を使用してください: http://hdl.handle.net/10119/18798

タイトル: Error-Exponent of Distributed Hypothesis Testing for Gilbert-Elliot Source Models
著者: Adamou, Ismaila Salihou
Dupraz, Elsa
Zribi, Amin
Matsumoto, Tad
発行日: 2023-10-10
出版者: Institute of Electrical and Electronics Engineers (IEEE)
誌名: International Symposium on Topics in Coding (ISTC 2023)
巻: 2023
開始ページ: 1
終了ページ: 5
DOI: 10.1109/ISTC57237.2023.10273559
抄録: This paper considers Distributed Hypothesis Testing (DHT), in which a source X is encoded given that a side information Y is available only to the decoder. The decoder’s task is to make a decision between two hypothesis H0 and H1 related to the joint probability distribution of X and Y . While most works on DHT have adopted an information-theoretic approach by providing generic error exponents for the decision error, we focus on a specific and practical source model: the Gilbert-Elliot (GE) model. The later is a two-states hidden Markov model which describes time-varying correlation between X and Y. Starting from the generic error exponents, we develop a method for numerically estimating the error exponent for the GE model. We provide numerical results to evaluate the impact of the model parameters on the error exponents and explore the tradeoff between two types of error events, namely the testing error and the binning error.
Rights: This is the author's version of the work. Copyright (C) 2023 IEEE. 2023 12th International Symposium on Topics in Coding (ISTC), Brest, France. DOI: 10.1109/ISTC57237.2023.10273559. 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.
URI: http://hdl.handle.net/10119/18798
資料タイプ: author
出現コレクション:b11-1. 会議発表論文・発表資料 (Conference Papers)

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