Improved Random-Binning Exponent for Distributed Hypothesis Testing

2023/06/26 by Yuval Kochman, Kochman, Yuval, Ligong Wang +1 · 2 citations
Computer Science · Mathematics · #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #Information Theory (cs.IT) #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.2306.14499

openalex publication_date 2023/06/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Consider the problem of distributed binary hypothesis testing with two terminals, where the decision is made at one of them (the "receiver"). We study the exponent of the error probability of the second type. Previously, an achievable exponent was derived by Shimokawa, Han, and Amari using a "quantization and binning" scheme. We propose a simple modification on the receiver's decision rule in this scheme to attain a better exponent.

Cited by

• Multi-Sensor Distributed Hypothesis Testing in the Low-Power Regime
• Practical Short-Length Coding Schemes for Binary Distributed Hypothesis Testing

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