Reinforcement Learning Optimization for Energy-Efficient Cellular Networks with Coordinated Multipoint Communications
Reinforcement Learning Optimization for Energy-Efficient Cellular Networks with Coordinated Multipoint Communications
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DOI：
10.1155/2014/698797
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发表时间：
2014-07
影响因子：
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通讯作者：
Huibin Lu;Baozhu Hu;Zhiyuan Ma;Shuhuan Wen
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文献类型：
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作者：
Huibin Lu;Baozhu Hu;Zhiyuan Ma;Shuhuan Wen
近来，存在解决无线通信的“能量效率”方面的新兴趋势。而协作多点（CoMP）通信是一种很有前途的提高能量效率的方法。然而，由于下行链路性能对用户也很重要，因此我们应该在保持完美的下行链路性能的同时提高能量效率。本文提出了一种控制理论的方法来研究的能量效率和下行链路的性能问题，在合作无线蜂窝网络与CoMP通信。具体来说，为了在CoMP中的节能传输中做出最佳基站分组的决策，我们开发了一种强化学习（RL）算法。我们应用RL算法的自学习来获得基站分组的最优策略，并在自学习开始时引入变量，以防止陷入局部最大值点。仿真结果表明了该方案的有效性和可行性。
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.