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

• 成果类型: Article
• 署名作者: Huang, Lingying; Wang, Jiazheng; Kung, Enoch; Mo, Yilin; Wu, Junfeng; Shi, Ling
• 署名单位: Hong Kong University of Science & Technology; University of London; University College London; Tsinghua University; Tsinghua University; Zhejiang University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.3007510
• 发表日期: 2021
• 页码: 2407-2414
• 关键词: uncertainty; schedules; Mobile handsets; Base stations; Random variables; State estimation; branch-and-bound algorithm; cognitive radio sensor network (CRSN); minimum mean squared error; stochastic event-based schedule

摘要

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 article, 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.