TACT: A Transfer Actor-Critic Learning Framework for Energy Saving in Cellular Radio Access Networks

R Li, Z Zhao, Xianfu Chen, J Palicot, H Zhang

    Research output: Contribution to journalArticleScientificpeer-review

    114 Citations (Scopus)


    Recent works have validated the possibility of improving energy efficiency in radio access networks (RANs), achieved by dynamically turning on/off some base stations (BSs). In this paper, we extend the research over BS switching operations, which should match up with traffic load variations. Instead of depending on the dynamic traffic loads which are still quite challenging to precisely forecast, we firstly formulate the traffic variations as a Markov decision process. Afterwards, in order to foresightedly minimize the energy consumption of RANs, we design a reinforcement learning framework based BS switching operation scheme. Furthermore, to speed up the ongoing learning process, a transfer actor-critic algorithm (TACT), which utilizes the transferred learning expertise in historical periods or neighboring regions, is proposed and provably converges. In the end, we evaluate our proposed scheme by extensive simulations under various practical configurations and show that the proposed TACT algorithm contributes to a performance jumpstart and demonstrates the feasibility of signi?cant energy ef?ciency improvement at the expense of tolerable delay performance
    Original languageEnglish
    Pages (from-to)2000-2011
    Number of pages11
    JournalIEEE Transactions on Wireless Communications
    Issue number4
    Publication statusPublished - 2014
    MoE publication typeA1 Journal article-refereed


    • Radio access networks
    • base stations
    • sleeping mode
    • green communications
    • energy saving
    • reinforcement learning
    • transfer learning
    • actor-critic algorithm


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