Skip to Main content Skip to Navigation
Journal articles

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

Abstract : 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 significant energy efficiency improvement at the expense of tolerable delay performance.
Complete list of metadata
Contributor : Myriam Andrieux Connect in order to contact the contributor
Submitted on : Thursday, October 9, 2014 - 3:00:38 PM
Last modification on : Thursday, January 20, 2022 - 12:54:10 PM

Links full text



Rongpeng Li, Zhifeng Zhao, Xianfu Chen, Jacques Palicot, Honggang Zhang. TACT: A Transfer Actor-Critic Learning Framework for Energy Saving in Cellular Radio Access Networks. IEEE Transactions on Wireless Communications, Institute of Electrical and Electronics Engineers, 2014, 13 (4), pp.2000-2011. ⟨10.1109/TWC.2014.022014.130840⟩. ⟨hal-01073320⟩



Les métriques sont temporairement indisponibles