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

Zhang, Xinruo and Zheng, Gan and Lambotharan, Sangarapillai and Nakhai, Mohammad Reza and Wong, Kai-Kit (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. ISSN 0090-6778

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Official URL: https://doi.org/10.1109/TCOMM.2020.2977895

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.

Item Type:	Article
Uncontrolled Keywords:	Libraries; Servers; Indexes; Microcell networks; Optimization; Heuristic algorithms; Gallium nitride; Non-stationary bandit; cache placement; content delivery; time-varying popularity; dynamic content library
Divisions:	Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor:	Elements
Depositing User:	Elements
Date Deposited:	09 Mar 2020 15:23
Last Modified:	15 Jan 2022 01:32
URI:	http://repository.essex.ac.uk/id/eprint/27060

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