Computation Rate Maximization for Wireless Powered Edge Computing With Multi-User Cooperation

• URL: http://arxiv.org/abs/2402.16866v1
• Date: Mon, 22 Jan 2024 05:22:19 GMT
• Title: Computation Rate Maximization for Wireless Powered Edge Computing With Multi-User Cooperation
• Authors: Yang Li, Xing Zhang, Bo Lei, Qianying Zhao, Min Wei, Zheyan Qu, Wenbo Wang,
• Abstract summary: This study considers a wireless-powered mobile edge computing system that includes a hybrid access point equipped with a computing unit and multiple Internet of Things (IoT) devices. We propose a novel muti-user cooperation scheme to improve computation performance, where collaborative clusters are dynamically formed. Specifically, we aims to maximize the weighted sum computation rate (WSCR) of all the IoT devices in the network.
• Score: 10.268239987867453
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The combination of mobile edge computing (MEC) and radio frequency-based wireless power transfer (WPT) presents a promising technique for providing sustainable energy supply and computing services at the network edge. This study considers a wireless-powered mobile edge computing system that includes a hybrid access point (HAP) equipped with a computing unit and multiple Internet of Things (IoT) devices. In particular, we propose a novel muti-user cooperation scheme to improve computation performance, where collaborative clusters are dynamically formed. Each collaborative cluster comprises a source device (SD) and an auxiliary device (AD), where the SD can partition the computation task into various segments for local processing, offloading to the HAP, and remote execution by the AD with the assistance of the HAP. Specifically, we aims to maximize the weighted sum computation rate (WSCR) of all the IoT devices in the network. This involves jointly optimizing collaboration, time and data allocation among multiple IoT devices and the HAP, while considering the energy causality property and the minimum data processing requirement of each device. Initially, an optimization algorithm based on the interior-point method is designed for time and data allocation. Subsequently, a priority-based iterative algorithm is developed to search for a near-optimal solution to the multi-user collaboration scheme. Finally, a deep learning-based approach is devised to further accelerate the algorithm's operation, building upon the initial two algorithms. Simulation results show that the performance of the proposed algorithms is comparable to that of the exhaustive search method, and the deep learning-based algorithm significantly reduces the execution time of the algorithm.

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