Content Placement Learning for Success Probability Maximization in Wireless Edge Caching Networks

Navneet Garg, Mathini Sellathurai, Tharmalingam Ratnarajah

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

To meet increasing demands of wireless multimedia communications, caching of important contents in advance is one of the key solutions. Optimal caching depends on content popularity in future which is unknown in advance. In this paper, modeling content popularity as a finite state Markov chain, reinforcement Q-learning is employed to learn optimal content placement strategy in homogeneous Poisson point process (PPP) distributed caching network. Given a set of available placement strategies, simulations show that the presented framework successfully learns and provides the best content placement to maximize the average success probability.
Original languageEnglish
Title of host publicationICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)978-1-4799-8131-1
DOIs
Publication statusPublished - 17 Apr 2019
Event44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
Duration: 12 May 201917 May 2019

Publication series

NameIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherIEEE
ISSN (Electronic)2379-190X

Conference

Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Country/TerritoryUnited Kingdom
CityBrighton
Period12/05/1917/05/19

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