Multi-Hop Network with Multiple Decision Centers under Expected-Rate Constraints
Mustapha Hamad, Mich\`ele Wigger, Mireille Sarkiss

TL;DR
This paper characterizes the fundamental limits of type-II error exponents in a multi-hop distributed hypothesis testing system with multiple decision centers under expected rate constraints, revealing a boosted exponents region compared to maximum-rate constraints.
Contribution
It introduces a new multiplexing and rate-sharing strategy for achieving the exponents region and provides novel converse proofs using change of measure arguments and asymptotic Markov chains.
Findings
Exponents region is larger under expected-rate constraints than maximum-rate constraints.
When all DCs have equal type-I error probabilities, the exponents region is rectangular.
Special cases for K=2 and K=3 are explicitly characterized.
Abstract
We consider a multi-hop distributed hypothesis testing problem with multiple decision centers (DCs) for testing against independence and where the observations obey some Markov chain. For this system, we characterize the fundamental type-II error exponents region, i.e., the type-II error exponents that the various DCs can achieve simultaneously, under expected rate-constraints. Our results show that this fundamental exponents region is boosted compared to the region under maximum-rate constraints, and that it depends on the permissible type-I error probabilities. When all DCs have equal permissible type-I error probabilities, the exponents region is rectangular and all DCs can simultaneously achieve their optimal type-II error exponents. When the DCs have different permissible type-I error probabilities, a tradeoff between the type-II error exponents at the different DCs arises. New…
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Taxonomy
TopicsDistributed Sensor Networks and Detection Algorithms · Wireless Communication Security Techniques · Statistical Methods and Inference
