Computation time minimized offloading in NOMA-enabled wireless powered mobile edge computing

Chen, W, Wei, X, Chi, K, Yu, K, Tolba, A, Mumtaz, S ORCID: https://orcid.org/0000-0001-6364-6149 and Guizani, M, 2024. Computation time minimized offloading in NOMA-enabled wireless powered mobile edge computing. IEEE Transactions on Communications. ISSN 0090-6778

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Abstract

Wireless powered mobile edge computing (WP-MEC), which combines mobile edge computing (MEC) and wireless power transfer (WPT), is a promising paradigm for coping with the computing power and energy constraints of wireless devices. However, how to realize the online optimal offloading decision and resource allocation in the WP-MEC system is very challenging. This paper studies the system computation completion time (SCCT) minimization problems for WP-MEC networks using non-orthogonal multiple access (NOMA) communication under binary and partial offloading modes. Due to the complexity of the optimization problems and the time-varying nature of the channel state information, we decouple the original problems into a top-problem of optimizing WPT duration and a sub-problem of optimizing resource allocation, and then propose a convolutional deep reinforcement learning online (CDRO) algorithm. For the top-problem, a deep reinforcement learning framework is used to obtain the near-optimal WPT duration, and an incremental exploration policy is designed to balance the exploration accuracy and exploration range to improve the convergence performance of the CDRO algorithm. For the sub-problems, we propose their corresponding low-complexity algorithms based on in-depth analysis and derivation of the optimal offloading decision’s properties. Finally, numerical results show that the proposed CDRO algorithm achieves near-optimal SCCT with low computational complexity, enabling online decision-making in time-varying channel environments.

Item Type:	Journal article
Publication Title:	IEEE Transactions on Communications
Creators:	Chen, W., Wei, X., Chi, K., Yu, K., Tolba, A., Mumtaz, S. and Guizani, M.
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Date:	24 May 2024
ISSN:	0090-6778
Identifiers:	Number Type 10.1109/TCOMM.2024.3405316 DOI 1897968 Other
Rights:	© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Divisions:	Schools > School of Science and Technology
Record created by:	Melissa Cornwell
Date Added:	30 May 2024 14:19
Last Modified:	30 May 2024 14:19
URI:	https://irep.ntu.ac.uk/id/eprint/51489

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