Energy saving through a learning framework in greener cellular radio access networks

R. Li, Z. Zhao, Xianfu Chen, H. Zhang

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

    13 Citations (Scopus)


    Recent works have validated the possibility of energy efficiency improvement in radio access networks (RAN), depending on dynamically turn on/off some base stations (BSs). In this paper, we extend the research over BS switching operation, matching up with traffic load variations. However, instead of depending on the predicted traffic loads, which is still quite challenging to precisely forecast, we formulate the traffic variation as a Markov decision process (MDP). Afterwards, in order to foresightedly minimize the energy consumption of RAN, we adopt the actor-critic method and design a reinforcement learning framework based BS switching operation scheme. In the end, we evaluate our proposed scheme by extensive simulations under various practical configurations and prove the feasibility of significant energy efficiency improvement.
    Original languageEnglish
    Title of host publicationProceedings of Globecom 2012
    PublisherIEEE Institute of Electrical and Electronic Engineers
    ISBN (Electronic)978-1-4673-0921-9
    ISBN (Print)978-1-4673-0920-2
    Publication statusPublished - 2012
    MoE publication typeA4 Article in a conference publication
    EventIEEE Global Communications Conference, GLOBECOM 2012 - Anaheim, CA, United States
    Duration: 3 Dec 20127 Dec 2012


    ConferenceIEEE Global Communications Conference, GLOBECOM 2012
    Abbreviated titleGLOBECOM 2012
    Country/TerritoryUnited States
    CityAnaheim, CA


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