Reinforcement Learning Optimization for Energy-Efficient Cellular Networks with Coordinated Multipoint Communications

Recently, there is an emerging trend of addressing "energy efficiency" aspect of wireless communications. And coordinated multipoint (CoMP) communication is a promising method to improve energy efficiency. However, since the downlink performance is also important for users, we should improve the energy efficiency as well as keeping a perfect downlink performance. This paper presents a control theoretical approach to study the energy efficiency and downlink performance issues in cooperative wireless cellular networks with CoMP communications. Specifically, to make the decisions for optimal base station grouping in energy-efficient transmissions in CoMP, we develop a Reinforcement Learning (RL) Algorithm. We apply the Q-learning of the RL Algorithm to get the optimal policy for base station grouping with introduction of variations at the beginning of the Q-learning to prevent Q from falling into local maximum points. Simulation results are provided to show the process and effectiveness of the proposed scheme.