DRL-based computation rate maximization for wireless powered multi-AP edge computing

Zhang, S, Bao, S, Chi, K, Yu, K and Mumtaz, S ORCID: https://orcid.org/0000-0001-6364-6149, 2023. DRL-based computation rate maximization for wireless powered multi-AP edge computing. IEEE Transactions on Communications. ISSN 0090-6778

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Abstract

In the ongoing 5G and upcoming 6G eras, the intelligent Internet of Things (IoT) network will take increasingly important responsibility for industrial production, daily life and so on. The IoT devices with limited battery size and computing ability cannot meet many applications brought out by the data-driven artificial intelligence technique. The combination of wireless power transfer (WPT) and edge computing is regarded as an effective solution to this dilemma. IoT devices can collect radio frequency energy provided by hybrid access points (HAPs) to process data locally or offload data to the edge servers of HAPs. However, how to efficiently make offloading decisions and allocate resource is challenging, especially for the networks with multiple HAPs. In this paper, we consider the sum computation rate maximization problem for a WPT empowered IoT network with multiple HAPs and IoT devices. The problem is formulated as a mixed-integer nonlinear programming problem. To solve this problem efficiently, we decompose it into a top-problem of optimizing offloading decisions and a sub-problem of optimizing time allocation under the given offloading decisions. We propose a deep reinforcement learning (DRL) based algorithm to output the near-optimal offloading decision and design an efficient algorithm based on Lagrangian duality method to obtain the consequent optimal time allocation. Simulations verified that the proposed DRL-based algorithm can achieve more than 95 percent of the maximal computation rate with low complexity. Compared with the common actor-critic algorithm, the proposed algorithm has the substantial advantage in convergence speed, achieved computation rate and running time.

Item Type:	Journal article
Publication Title:	IEEE Transactions on Communications
Creators:	Zhang, S., Bao, S., Chi, K., Yu, K. and Mumtaz, S.
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Date:	2023
ISSN:	0090-6778
Identifiers:	Number Type 10.1109/tcomm.2023.3325905 DOI 1824881 Other
Rights:	© 2023 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:	Jeremy Silvester
Date Added:	25 Oct 2023 11:11
Last Modified:	23 Nov 2023 11:01
URI:	https://irep.ntu.ac.uk/id/eprint/50125

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