Online Learning for Distributed Computation Offloading in Wireless Powered Mobile Edge Computing Networks

• Published in: IEEE transactions on parallel and distributed systems Vol. 33; no. 8; pp. 1841 - 1855
• Main Authors: Wang, Xiaojie, Ning, Zhaolong, Guo, Lei, Guo, Song, Gao, Xinbo, Wang, Guoyin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Computation offloading; delay minimization; Delays; Design; Distance learning; distributed execution; Edge computing; Machine learning; Mobile computing; Multi-access edge computing; Networks; online learning; Optimization; Processor scheduling; Resource management; Servers; Synchronism; Task analysis; Task scheduling; Wireless power transmission; Wireless powered mobile edge computing
• ISSN: 1045-9219; 1558-2183
• DOI: 10.1109/TPDS.2021.3129618

A novel paradigm named Wireless Powered Mobile Edge Computing (WP-MEC) emerges recently, which integrates Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT) technologies. It enables mobile clients to both extend their computing capacities by task offloading, and charge from edge servers via energy transmission. Existing studies generally focus on the centralized design of task scheduling and energy charging in WP-MEC networks. To meet the decentralization requirement of the near-coming 6G network, we propose an online learning algorithm for computation offloading in WP-MEC networks with a distributed execution manner. Specifically, we first define the delay minimization problem by considering task deadline and energy constraints. Then, we transform it into a primal-dual optimization problem based on the Bellman equation. After that, we design a novel neural model that learns both offloading and time division decisions in each time slot to solve the formulated optimization problem. To train and execute the designed algorithm distributivity, we form multiple learning models decentralized on edge servers and they work coordinately to achieve parameter synchronization. At last, both theoretical and performance analyses show that the designed algorithm has significant advantages in comparison with other representative schemes.