A Learning Approach for Joint Design of Event-triggered Control and Power-Efficient Resource Allocation
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Abstract (arXiv)
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 textcolorbluewith 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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 12 of its 40 references that are in the map. First posted on arXiv 14 May 2022.
Builds on
12 of 40 references.
References · 12
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- 2018DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Schedulingcited by 8
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- 2020Cascade Attribute Network: Decomposing Reinforcement Learning Control Policies using Hierarchical Neural Networkscited by 4
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Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.