Improving energy efficiency in green femtocell networks: A hierarchical reinforcement learning framework

Xianfu Chen, Honggang Zhang, Tao Chen, Mika Lasanen

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

    28 Citations (Scopus)


    This paper investigates energy efficiency for two-tier femtocell networks through combining game theory and stochastic learning. With the Stackelberg game formulation, a hierarchical reinforcement learning framework is applied to studying the joint average utility maximization of macrocells and femtocells subject to the minimum signal-to-interference-plus-noise-ratio requirements. The macrocells behave as the leaders and the femtocells are followers during the learning procedure. At each time step, the leaders commit to dynamic strategies based on the best responses of the followers, while the followers compete against each other with no further information but the leaders' strategy information. In this paper, we propose two learning algorithms to schedule each cell's stochastic power levels, leading by the macrocells. Numerical experiments are presented to validate the proposed studies and show that the two learning algorithms substantially improve the energy efficiency of the femtocell networks.
    Original languageEnglish
    Title of host publication2013 IEEE International Conference on Communications (ICC)
    PublisherIEEE Institute of Electrical and Electronic Engineers
    ISBN (Print)978-1-4673-3122-7
    Publication statusPublished - 2013
    MoE publication typeA4 Article in a conference publication
    EventIEEE International Conference on Communications, ICC 2013 - Budapest, Hungary
    Duration: 9 Jun 201313 Jun 2013


    ConferenceIEEE International Conference on Communications, ICC 2013
    Abbreviated titleICC 2013


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