Optimal Exponents In Cascaded Hypothesis Testing under Expected Rate Constraints
Mustapha Hamad, Mich\`ele Wigger, Mireille Sarkiss

TL;DR
This paper characterizes the optimal error exponents in a cascaded hypothesis testing setup with two decision centers under expected rate constraints, revealing a tradeoff between error probabilities when type-I constraints differ.
Contribution
It provides a new converse proof and a matching achievability scheme for the general case of unequal type-I error constraints in cascaded hypothesis testing.
Findings
Optimal type-II error exponents are characterized under expected rate constraints.
A tradeoff exists between maximum type-II error probabilities when type-I constraints are unequal.
Previous results do not show such a tradeoff under equal or maximum rate constraints.
Abstract
Cascaded binary hypothesis testing is studied in this paper with two decision centers at the relay and the receiver. All terminals have their own observations, where we assume that the observations at the transmitter, the relay, and the receiver form a Markov chain in this order. The communication occurs over two hops, from the transmitter to the relay and from the relay to the receiver. Expected rate constraints are imposed on both communication links. In this work, we characterize the optimal type-II error exponents at the two decision centers under constraints on the allowed type-I error probabilities. Our recent work characterized the optimal type-II error exponents in the special case when the two decision centers have same type-I error constraints and provided an achievability scheme for the general setup. To obtain the exact characterization for the general case, in this paper we…
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