A Learning Approach for Joint Design of Event-Triggered Control and Power-Efficient Resource Allocation

• Published in: IEEE transactions on vehicular technology 2022-06, Vol.71 (6), p.6322-6334
• Main Authors: Termehchi, Atefeh, Rasti, Mehdi
• Format: Article
• Language: English
• Subjects: 5G mobile communication; Actuators; Cyber-physical systems; Delays; Downlink; Downlinking; Event triggered control; hierarchical reinforcement learning; Industrial cyber-physical system; Learning; Multiple objective analysis; Optimization; Power consumption; power efficient network; Quality of service; radio resource allocation; Resource allocation; Resource management; Ultra reliable low latency communication; Wireless networks
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2022.3159739

In emerging Industrial Cyber-Physical Systems (ICPSs), the joint design of communication and control sub-systems is essential, as these sub-systems are interconnected. In this paper, we study the joint design problem of an event-triggered control and an energy-efficient resource allocation in a fifth generation (5 G) wireless network. We formally state the problem as a multi-objective optimization one, aiming to minimize the number of updates on the actuators' input and the power consumption in the downlink transmission. To address the problem, we propose a model-free hierarchical reinforcement learning approach with uniformly ultimate boundedness stability guarantee that learns four policies simultaneously. These policies contain an update time policy on the actuators' input, a control policy, and energy-efficient sub-carrier and power allocation policies. Our simulation results show that the proposed approach can properly control a simulated ICPS and significantly decrease the number of updates on the actuators' input as well as the downlink power consumption.