Enhancing Energy Efficiency in Wireless-Powered MEC Systems through Lyapunov-Guided Deep Reinforcement Learning
Zhu, Bincheng Huang, Liang Chi, Kaikai Alharbi, Abdullah Yu, Keping Guizani, Mohsen
Zhu, Bincheng
Huang, Liang
Chi, Kaikai
Alharbi, Abdullah
Yu, Keping
Guizani, Mohsen
Supervisor
Department
Machine Learning
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Type
Journal article
Date
2025
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Language
English
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Abstract
This paper addresses long-term energy efficiency in a wireless power transfer-enabled mobile-edge computing (MEC) system, facing challenges from time-varying channels and stochastic task arrivals. We formulate the problem to optimize offloading, power transfer duration, and energy consumption while ensuring queue stability. We propose a novel Lyapunov-guided deep reinforcement learning (LyCNN-DRL) algorithm to efficiently solve the long-term mixed integer non-linear programming problem without prior knowledge of future conditions. The approach decomposes the problem into resource allocation and binary offloading components, using a convolutional neural network for near-optimal offloading decisions and the Lagrange dual function for optimal resource allocation. Extensive simulations show that LyCNN-DRL outperforms benchmark algorithms in energy efficiency and latency, achieving over 97% of the optimal utility while reducing execution latency to approximately 50 milliseconds in ten-WD networks. Additionally, we derive the trade-off between energy efficiency and queue length as [O(1/V),O(V)], where V is the Lyapunov control parameter.
Citation
B. Zhu, L. Huang, K. Chi, A. Alharbi, K. Yu and M. Guizani, "Enhancing Energy Efficiency in Wireless-Powered MEC Systems through Lyapunov-Guided Deep Reinforcement Learning," in IEEE Transactions on Wireless Communications, doi: 10.1109/TWC.2025.3561167
Source
IEEE Transactions on Wireless Communications
Conference
Keywords
Energy consumption, Resource management, Energy efficiency, Optimization, Wireless communication, Time division multiple access, Computational modeling, Time-frequency analysis, Servers, Radio frequency, Lyapunov optimization, Mobile-edge computing (MEC), Convex optimization, Reinforcement learning
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Publisher
IEEE