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.

无线供电移动边缘计算系统中，采用一种新颖的多用户合作方案，通过动态形成协作群组来提高计算性能，并通过两种初始算法和深度学习方法进行优化和加速。