Distributed Hypothesis Testing over a Noisy Channel: Error-Exponents Trade-Off

A two-terminal distributed binary hypothesis testing problem over a noisy channel is studied. The two terminals, called the observer and the decision maker, each has access to n independent and identically distributed samples, denoted by U and V, respectively. The observer communicates to the decision maker over a discrete memoryless channel, and the decision maker performs a binary hypothesis test on the joint probability distribution of (U,V) based on V and the noisy information received from the observer. The trade-off between the exponents of the type I and type II error probabilities is investigated. Two inner bounds are obtained, one using a separation-based scheme that involves type-based compression and unequal error-protection channel coding, and the other using a joint scheme that incorporates type-based hybrid coding. The separation-based scheme is shown to recover the inner bound obtained by Han and Kobayashi for the special case of a rate-limited noiseless channel, and also the one obtained by the authors previously for a corner point of the trade-off. Finally, we show via an example that the joint scheme achieves a strictly tighter bound than the separation-based scheme for some points of the error-exponents trade-off.

Automated summary

This paper investigates a two-terminal distributed binary hypothesis testing problem over a noisy channel. The observer and decision maker each have access to independent and identically distributed samples. The observer communicates with the decision maker through a discrete memoryless channel. The trade-off between the exponents of the type I and type II error probabilities is studied, and two inner bounds are obtained using separation-based and joint schemes respectively. The results show that the joint scheme achieves a tighter bound than the separation-based scheme for certain points of the error-exponents trade-off.