Du, J., Gong, J., Chu, X. orcid.org/0000-0003-1863-6149 et al. (4 more authors) (2025) QoE-guaranteed optimization in MEC-enabled metaverse: an active inference deep reinforcement learning approach. IEEE Transactions on Cognitive Communications and Networking. ISSN 2332-7731
Abstract
In this paper, we consider a MEC-enabled metaverse scenario which consists of a remote metaverse server and an edge server that cooperates to provide services to mobile users. The edge server is deployed at the base station (BS), serves a dual role: augmenting computational capabilities for user equipment (UE) and pre-caching a portion of the metaverse service contents before each time slot. Moreover, the foreground information and the requested contents generated by the UEs can also be cached to the BS. We formulate a problem to maximize the cache hit number by jointly optimizing contents pre-caching and resource allocation at the BS while considering UEs preference and reducing the UEs total energy consumption, essential for the efficient delivery of services in dynamic MEC environments. To solve this problem, we reformulate it as a partially observable markov decision process and propose an active inference enabled deep reinforcement learning algorithm, which combines active inference with deep reinforcement learning to select the optimal strategy by minimizing the expected free energy. Simulations show that the proposed algorithm can effectively improve the total quality of experience and the cache hit number of UEs, while minimizing the UEs total energy consumption compared with other baseline algorithms
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2025 The Authors. Except as otherwise noted, this author-accepted version of a journal article published in IEEE Transactions on Cognitive Communications and Networking is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ |
Keywords: | Mobile edge computing; metaverse; deep reinforcement learning; active inference |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > School of Electrical and Electronic Engineering |
Funding Information: | Funder Grant number UK RESEARCH AND INNOVATION 101086219 EP/X038971/1 UK Research and Innovation EP/X038971/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 07 Apr 2025 08:07 |
Last Modified: | 21 May 2025 15:42 |
Status: | Published online |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/TCCN.2025.3554003 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:225219 |