Deep Reinforcement Learning for cell on/off energy saving on Wireless Networks

Abstract

Increased network traffic demands have led to ex-tremely dense network deployments. This translates to significant growth in energy consumption at the radio access networks, resulting in high network operation costs (OPEX). In this work, we apply deep reinforcement learning to reduce the energy consumption at the base station in dense wireless networks, by allowing cells that overlap in geographical areas to be put in standby mode according to the changing network conditions. We start by formulating the problem of the cell on/off energy saving in dense wireless networks as a Markov decision process. Then, a deep reinforcement learning (DRL) solution is proposed. This DRL solution takes into account different key performance indicators (KPIs) of both the network and user equipment and aims to reduce the energy consumed by the network without significantly impacting the overall KPIs. The performance of the proposed solution is evaluated using a practical network simulator.