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

• URL: http://arxiv.org/abs/2205.07070v1
• Date: Sat, 14 May 2022 14:16:11 GMT
• Title: A Learning Approach for Joint Design of Event-triggered Control and Power-Efficient Resource Allocation
• Authors: Atefeh Termehchi, Mehdi Rasti
• Abstract summary: We study the joint design problem of an event-triggered control and an energy-efficient resource allocation in a fifth generation (5G) wireless network. We propose a model-free hierarchical reinforcement learning approach that learns four policies simultaneously. 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.
• Score: 3.822543555265593
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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 (5G) 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 \textcolor{blue}{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.

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