Improved Random-Binning Exponent for Distributed Hypothesis Testing
Yuval Kochman, Ligong Wang · IEEE Transactions on Information Theory · 2025
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.