Double deep Q-network based joint edge caching and content recommendation with inconsistent file sizes in fog-RANs

Yan, J, Zhang, M, Jiang, Y, Zheng, F-C, Chang, Q, Abualnaja, KM, Mumtaz, S ORCID: https://orcid.org/0000-0001-6364-6149 and You, X, 2023. Double deep Q-network based joint edge caching and content recommendation with inconsistent file sizes in fog-RANs. IEEE Transactions on Vehicular Technology. ISSN 0018-9545

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Abstract

In this paper, the joint edge caching and content recommendation problem for the case of inconsistent file sizes is investigated for fog radio access networks (F-RANs). Firstly, we transform the joint caching and recommendation policy into a ‘single’ caching policy. Then, a time-varying personalized user request model is proposed to describe the fluctuant demands of users. To maximize the long-term net profit of each fog access point (F-AP), we formulate the caching optimization problem and resort to a reinforcement learning (RL) framework. To address the impact of file size inconsistency, a ‘pre-split’ mechanism with a dynamic upper limit is adopted to meet the constraint of storage capacity, and a ‘lazy’ updating mechanism is introduced into the training process. Finally, a double deep Q-network (DDQN) based distributed edge caching algorithm is proposed with content recommendation. Simulation results show that compared with the existing methods, the average net profit of our proposed algorithm can be increased up to 29.7% while content recommendation can not only increase caching efficiency but also accelerate convergence.

Item Type:	Journal article
Publication Title:	IEEE Transactions on Vehicular Technology
Creators:	Yan, J., Zhang, M., Jiang, Y., Zheng, F.-C., Chang, Q., Abualnaja, K.M., Mumtaz, S. and You, X.
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Date:	30 October 2023
ISSN:	0018-9545
Identifiers:	Number Type 10.1109/tvt.2023.3328554 DOI 1831595 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:	Laura Ward
Date Added:	06 Nov 2023 16:57
Last Modified:	06 Nov 2023 16:57
URI:	https://irep.ntu.ac.uk/id/eprint/50288

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