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A Reinforcement Learning-Based User-Assisted Caching Strategy for Dynamic Content Library in Small Cell Networks

Zhang, X; Zheng, G; Lambotharan, S; Nakhai, MR; Wong, KK; (2020) A Reinforcement Learning-Based User-Assisted Caching Strategy for Dynamic Content Library in Small Cell Networks. IEEE Transactions on Communications , 68 (6) pp. 3627-3639. 10.1109/TCOMM.2020.2977895. Green open access

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Abstract

This paper studies the problem of joint edge cache placement and content delivery in cache-enabled small cell networks in the presence of spatio-temporal content dynamics unknown a priori. The small base stations (SBSs) satisfy users' content requests either directly from their local caches, or by retrieving from other SBSs' caches or from the content server. In contrast to previous approaches that assume a static content library at the server, this paper considers a more realistic non-stationary content library, where new contents may emerge over time at different locations. To keep track of spatio-temporal content dynamics, we propose that the new contents cached at users can be exploited by the SBSs to timely update their flexible cache memories in addition to their routine off-peak main cache updates from the content server. To take into account the variations in traffic demands as well as the limited caching space at the SBSs, a user-assisted caching strategy is proposed based on reinforcement learning principles to progressively optimize the caching policy with the target of maximizing the weighted network utility in the long run. Simulation results verify the superior performance of the proposed caching strategy against various benchmark designs.

Type: Article
Title: A Reinforcement Learning-Based User-Assisted Caching Strategy for Dynamic Content Library in Small Cell Networks
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TCOMM.2020.2977895
Publisher version: https://doi.org/10.1109/TCOMM.2020.2977895
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
Keywords: Non-stationary bandit, cache placement, content delivery, time-varying popularity, dynamic content library
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10104021
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