TWC_Xinruo_draft_v2.pdf (1.23 MB)

A reinforcement learning-based user-assisted caching strategy for dynamic content library in small cell networks

Download (1.23 MB)
journal contribution
posted on 19.11.2020, 14:12 by Xinruo Zhang, Gan Zheng, Sangarapillai Lambotharan, Mohammad Reza Nakhai, Kai-Kit Wong
© 1972-2012 IEEE. 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.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

IEEE Transactions on Communications

Volume

68

Issue

6

Pages

3627 - 3639

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

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

Acceptance date

23/02/2020

Publication date

2020-03-02

Copyright date

2020

ISSN

0090-6778

eISSN

1558-0857

Language

en

Depositor

Dr Gan Zheng Deposit date: 16 November 2020

Exports