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Lyapunov-Based Computation Rate Maximization for Wireless Powered Edge Computing
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DOI：10.1109/MSN60784.2023.00041.png)
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Abstract
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In recent years, wireless powered mobile edge computing (WP-MEC) is one of solutions to the problem of insufficient computing power and battery capacity of current edge devices (EDs). In this paper, we consider a WP-MEC network with multiple EDs and study the problem of maximizing the long-term computation rate of the system under the premise of maintaining the stability of the system data queue. Specifically, the objective function is described as a complex non-convex problem, we used Lyapunov optimization theory to decouple the multi-stage continuous random problem into sub-problems of deterministic frames. The problem of determining frames requires joint optimization of wireless power transfer (WPT) duration, local computing frequency, transmission duration, and energy required for offloading. In order to efficiently optimize these variables, we design a DRL-based algorithm combined with the CVX solver, the DRL algorithm learns the WPT duration, and the convex optimization algorithm obtains the system offloading strategy. From the simulation results, our algorithm can achieve performance close to that of the one-dimension exhaustive search algorithm while ensuring the stability of the data queue.
Keywords:
wireless powered mobile edge computing
resource allocation
reinforcement learning
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