2021/01/27 by Abanoub M. Girgis, Jihong Park, Girgis, Abanoub M. +5 · 2 citations
Computer Science · Engineering · Mathematics · #Age of Information Optimization #Energy Harvesting in Wireless Networks #FOS: Computer and information sciences #Information Theory (cs.IT) #IoT Networks and Protocols #cs.IT #math.IT
paper · pdf · doi:10.48550/arxiv.2101.11647
arxiv created 2021/01/27 · openalex publication_date 2021/01/27 · arxiv updated 2021/01/29 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
This article studies the joint problem of uplink-downlink scheduling and power allocation for controlling a large number of actuators that upload their states to remote controllers and download control actions over wireless links. To overcome the lack of wireless resources, we propose a machine learning-based solution, where only a fraction of actuators is controlled, while the rest of the actuators are actuated by locally predicting the missing state and/or action information using the previous uplink and/or downlink receptions via a Gaussian process regression (GPR). This GPR prediction credibility is determined using the age-of-information (AoI) of the latest reception. Moreover, the successful reception is affected by the transmission power, mandating a co-design of the communication and control operations. To this end, we formulate a network-wide minimization problem of the average AoI and transmission power under communication reliability and control stability constraints. To solve the problem, we propose a dynamic control algorithm using the Lyapunov drift-plus-penalty optimization framework. Numerical results corroborate that the proposed algorithm can stably control 2x more number of actuators compared to an event-triggered scheduling baseline with Kalman filtering and frequency division multiple access, which is 18x larger than a round-robin scheduling baseline.