Reinforcement learning for energy-efficient edge caching in mobile edge networks

Hantong Zheng, Huan Zhou, Ning Wang, Peng Chen, Shouzhi Xu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

Edge caching has become a promising application paradigm in 5G networks, which can support the explosive growth of Internet of Things (IoTs) services and applications by caching content at the edge of the mobile network to alleviate redundant traffic. In this paper, we consider the energy minimization problem in a heterogeneous network with edge caching technique. We formulate the content caching optimization problem as a Mixed Integer Non-Linear Programming (MINLP) problem, aiming to minimize the total system energy consumption with considering the energy consumption of users, Small Base Stations (SBSs) and Macro Base Stations (MBS). We model the optimization problem as a Markov Decision Process (MDP). Then, we propose a Q-learning based method to solve the optimization problem. Simulation results show that our proposed Q-learning method can significantly reduce the total system energy consumption in different scenarios compared with other benchmark methods.

Original languageEnglish
Title of host publicationIEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665404433
DOIs
StatePublished - 10 May 2021
Externally publishedYes
Event2021 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021 - Virtual, Online
Duration: 9 May 202112 May 2021

Publication series

NameIEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021

Conference

Conference2021 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021
CityVirtual, Online
Period9/05/2112/05/21

Keywords

  • Edge Caching
  • Energy consumption
  • Internet of Things
  • Markov Decision Process
  • Q-learning

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