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The main contribution of this paper is a strong converse result for $K$-hopdistributed hypothesis testing against independence with multiple(intermediate) decision centers under a Markov condition. Our result shows thatthe set of type-II error exponents that can simultaneously be achieved at allthe terminals does not depend on the maximum permissible type-I errorprobabilities. Our strong converse proof is based on a change of measureargument and on the asymptotic proof of specific Markov chains. This proofmethod can also be used for other converse proofs, and is appealing because itdoes not require resorting to variational characterizations or blowing-upmethods as in previous related proofs.

Strong Converses using Change of Measure and Asymptotic Markov Chains