Deep Reinforcement Learning for Energy-Efficient Power Control in Heterogeneous Networks

In a typical heterogeneous network (HetNet), in which a macro base station (BS) and multiple small BSs coexist on the same spectrum band, energy-efficiency (EE) performance is an important design metric and is highly related to the transmit power of BSs. Conventional methods optimize BSs' transmit power to enhance the EE by assuming that the global channel state information (CSI) is available. However, it is challenging or expensive to collect the instantaneous global CSI in the HetNet. In this paper, we utilize deep reinforcement learning (DRL) technique to design an intelligent power control algorithm, with which each BS can independently determine the transmit power based on only local information. Simulation results demonstrate that the proposed algorithm outperforms conventional methods in terms of both EE performance and time complexity.