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Contextual learning for content caching with unknown time-varying popularity profiles via incremental clustering

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journal contribution
posted on 15.02.2021, 11:36 by Xingchi Liu, Mahsa DerakhshaniMahsa Derakhshani, Sangarapillai LambotharanSangarapillai Lambotharan
With the rapid development of social networks and high-quality video sharing services, the demand for delivering large quantity and high quality contents under stringent endto-end delay requirement is increasing. To meet this demand, we study the content caching problem modelled as a Markov decision process in the network edge server when the popularity profiles are unknown and time-varying. In order to adapt to the changing trends of content popularity, a context-aware popularity learning algorithm is proposed. We prove that the learning error of this scheme is sublinear in the number of requests. In light of the learned popularities, a reinforcement learningbased caching scheme is designed on top of the state-actionreward-state-action algorithm with a function approximation. A reactive caching algorithm is also proposed to reduce the complexity. The time complexities of both the caching schemes are studied to demonstrate their feasibility in real time systems and a theoretical analysis is performed to prove that the cache hit rate of the reactive caching algorithm asymptotically converges to the optimal cache hit rate. Finally the simulations are presented to demonstrate the superiority of the proposed algorithms.

Funding

Royal Academy of Engineering under the Leverhulme Trust Research Fellowship scheme (DerakhshaniLTRF1920\16\67)

Communications Signal Processing Based Solutions for Massive Machine-to-Machine Networks (M3NETs)

Engineering and Physical Sciences Research Council

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History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

IEEE Transactions on Communications

Volume

69

Issue

5

Pages

3011 - 3024

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

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

03/02/2021

Publication date

2021-02-12

Copyright date

2021

ISSN

0090-6778

eISSN

1558-0857

Language

en

Depositor

Dr Mahsa Derakhshani. Deposit date: 14 February 2021