Intelligent base station management in greener traffic-aware cellular networks

Traffic-aware cellular networks dynamically turn on/off some base stations (BSs) according to the predicted traffic variation pattern and thus are able to improve the energy efficiency while providing plenty of network capacity. In this paper, instead of depending on the predicted traffic knowledge, we formulate the traffic variations as a Markov chain and design an intelligent BS management scheme with the aid of reinforcement learning framework. Specifically, we propose a Transfer Actor-CriTic (TACT) algorithm, which leverages the concept of transfer learning and exploits the transferred learning expertise from historical periods or neighboring regions to obtain better energy saving performance.