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Mathematical Problems in Engineering
Volume 2014, Article ID 698797, 9 pages
Research Article

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

1College of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066000, China
2The Key Laboratory for Special Fiber and Fiber Sensor of Hebei, Qinhuangdao, Hebei 066000, China
3Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066000, China

Received 18 April 2014; Accepted 3 July 2014; Published 15 July 2014

Academic Editor: Xue Jun Li

Copyright © 2014 Huibin Lu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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 -learning of the RL Algorithm to get the optimal policy for base station grouping with introduction of variations at the beginning of the -learning to prevent from falling into local maximum points. Simulation results are provided to show the process and effectiveness of the proposed scheme.