Reinforcement learning for 5G caching with dynamic cost

Alireza Sadeghi, Fatemeh Sheikholeslami, Antonio G. Matrques, Gergios B. Giannakis

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

2 Scopus citations

Abstract

In next generation cellular networks (5G) the access points (APs) are anticipated to be equipped with storage devices to serve locally requests for reusable popular contents by caching them at the edge of the network. The ultimate goal is to shift part of the load on the back-haul links from on-peak to off-peak periods, contributing to a better overall network performance and service experience. In order to enable the APs with efficient (optimal) fetch-cache decision making schemes able to work in dynamic settings, we introduce simple but flexible generic time-varying fetching and caching costs, which are then used to formulate a constrained minimization of the aggregate cost across files and time. Since caching decisions in every time slot influence the content availability in future instants, the novel formulation for optimal fetch-cache decisions falls into the class of dynamic programming, for which efficient reinforcement-learning-based solvers are proposed. The performance of our algorithms is assessed via numerical tests, and discussions on the inherent fetching-versus-caching trade-off are provided.

Original languageEnglish (US)
Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6653-6657
Number of pages5
ISBN (Print)9781538646588
DOIs
StatePublished - Sep 10 2018
Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada
Duration: Apr 15 2018Apr 20 2018

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2018-April
ISSN (Print)1520-6149

Other

Other2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
CountryCanada
CityCalgary
Period4/15/184/20/18

Bibliographical note

Funding Information:
The work in this paper has been supported by USA NSF grants 1423316, 1508993, 1514056, 1711471, and by the Spanish MINECO grant OMI-CROM (TEC2013-41604-R).

Keywords

  • Caching
  • Dynamic Programming
  • Dynamic pricing
  • Fetching
  • Resource allocation

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