English

Distributed Hypothesis Testing with Concurrent Detections

Information Theory 2018-05-17 v1 math.IT

Abstract

A detection system with a single sensor and K\mathsf{K} detectors is considered, where each of the terminals observes a memoryless source sequence and the sensor sends a common message to all the detectors. The communication of this message is assumed error-free but rate-limited. The joint probability mass function (pmf) of the source sequences observed at the terminals depends on an M\mathsf{M}-ary hypothesis (MK)(\mathsf{M} \geq \mathsf{K}), and the goal of the communication is that each detector can guess the underlying hypothesis. Each detector kk aims to maximize the error exponent under hypothesis kk, while ensuring a small probability of error under all other hypotheses. This paper presents an achievable exponents region for the case of positive communication rate, and characterizes the optimal exponents region for the case of zero communication rate. All results extend also to a composite hypothesis testing scenario.

Keywords

Cite

@article{arxiv.1805.06212,
  title  = {Distributed Hypothesis Testing with Concurrent Detections},
  author = {Pierre Escamilla and Michèle Wigger and Abdellatif Zaidi},
  journal= {arXiv preprint arXiv:1805.06212},
  year   = {2018}
}

Comments

5 pages, 2 figures, accepted for presentation at the 2018 IEEE International Symposium on Information Theory (ISIT 2018), Jun. 2018, Vail Colorado