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arXiv · 2003.10332

Stochastic Event-based Sensor Schedules for Remote State Estimation in Cognitive Radio Sensor Networks

Lingying Huang·Jiazheng Wang·Enoch Kung·Yilin Mo·Junfeng Wu·Ling Shi
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Abstract

We consider the problem of communication allocation for remote state estimation in a cognitive radio sensor network~(CRSN). A sensor collects measurements of a physical plant, and transmits the data to a remote estimator as a secondary user (SU) in the shared network. The existence of the primal users (PUs) brings exogenous uncertainties into the transmission scheduling process, and how to design an event-based scheduling scheme considering these uncertainties has not been addressed in the literature. In this work, we start from the formulation of a discrete-time remote estimation process in the CRSN, and then analyze the hidden information contained in the absence of data transmission. In order to achieve a better tradeoff between estimation performance and communication consumption, we propose both open-loop and closed-loop schedules using the hidden information under a Bayesian setting. The open-loop schedule does not rely on any feedback signal but only works for stable plants. For unstable plants, a closed-loop schedule is designed based on feedback signals. The parameter design problems in both schedules are efficiently solved by convex programming. Numerical simulations are included to illustrate the theoretical results.

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Lingying Huang, Jiazheng Wang, Enoch Kung, Yilin Mo, Junfeng Wu, Ling Shi. 2020-03-23. Stochastic Event-based Sensor Schedules for Remote State Estimation in Cognitive Radio Sensor Networks. https://doi.org/10.1109/tac.2020.3007510

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