2021/11/29 by Minh Thanh Vu, Vu, Minh Thanh
Computer Science · Engineering · Mathematics · #68P30 #Algorithms and Data Compression #FOS: Computer and information sciences #Information Theory (cs.IT) #Markov Chains and Monte Carlo Methods #Wireless Communication Security Techniques
paper · pdf · doi:10.48550/arxiv.2111.14279
openalex publication_date 2021/11/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper we revisit the binary hypothesis testing problem with one-sided compression. Specifically we assume that the distribution in the null hypothesis is a mixture distribution of iid components. The distribution under the alternative hypothesis is a mixture of products of either iid distributions or finite order Markov distributions with stationary transition kernels. The problem is studied under the Neyman-Pearson framework in which our main interest is the maximum error exponent of the second type of error. We derive the optimal achievable error exponent and under a further sufficient condition establish the maximum ε-achievable error exponent. It is shown that to obtain the latter, the study of the exponentially strong converse is needed. Using a simple code transfer argument we also establish new results for the Wyner-Ahlswede-Körner problem in which the source distribution is a mixture of iid components.