A New Upper Bound for Distributed Hypothesis Testing Using the Auxiliary Receiver Approach

TL;DR

This paper introduces a new, less assumption-dependent upper bound for distributed hypothesis testing using an auxiliary receiver, which is tighter or comparable to existing bounds and can outperform them in specific Gaussian scenarios.

Contribution

It presents a novel upper bound leveraging the auxiliary receiver approach with fewer assumptions and improved performance in certain cases.

Findings

The new bound is at least as tight as Rahman and Wagner's bound.

It requires fewer assumptions than previous bounds.

It outperforms existing bounds in some Gaussian settings.

Abstract

This paper employs the add-and-subtract technique of the auxiliary receiver approach to establish a new upper bound for the distributed hypothesis testing problem. This new bound has fewer assumptions than the upper bound proposed by Rahman and Wagner, is at least as tight as the bound by Rahman and Wagner, and can outperform it in certain Gaussian settings. Conceptually speaking, unlike Rahman and Wagner, who view their additional receiver as side information, we view it as an auxiliary receiver and use a different manipulation for single-letterization.