基于FQLC的异构密集蜂窝网络容量与覆盖联合优化
发布时间：2018-08-02 11:56
【摘要】：针对异构蜂窝网络下微蜂窝密集部署的不规则性,提出了一种基于强化学习的自优化控制系统,通过微蜂窝功率控制解决微蜂窝密集部署下的网络的容量与覆盖问题。将模糊逻辑与Q学习算法相结合,综合考虑网络的平均用户性能、边缘用户性能和网络环境相互影响来设计模糊逻辑与Q学习算法的联合瞬时回报奖惩值,进行网络容量与覆盖的联合自优化。仿真结果表明,该方法能实现密集化微蜂窝部署下的容量与覆盖自优化,有效提高系统平均用户吞吐量和边缘用户吞吐量。
[Abstract]:Aiming at the irregularity of microcellular dense deployment in heterogeneous cellular networks, a self-optimization control system based on reinforcement learning is proposed, which solves the network capacity and coverage problems under microcellular dense deployment through microcellular power control. Combining fuzzy logic with Q learning algorithm, considering the average user performance of network, edge user performance and network environment interaction, the combined instantaneous reward and punishment value of fuzzy logic and Q learning algorithm is designed. The joint self-optimization of network capacity and coverage is carried out. Simulation results show that this method can achieve capacity and coverage self-optimization under intensive microcellular deployment, and effectively improve the average user throughput and edge user throughput of the system.
【作者单位】： 重庆邮电大学移动通信技术重点实验室;
【基金】：国家“863”计划资助项目(2014AA0101A701) 国家科技重大专项资助项目(2014ZX03003010-004) 国家自然科学基金资助项目(6157103)
【分类号】：TN929.5
[Abstract]:Aiming at the irregularity of microcellular dense deployment in heterogeneous cellular networks, a self-optimization control system based on reinforcement learning is proposed, which solves the network capacity and coverage problems under microcellular dense deployment through microcellular power control. Combining fuzzy logic with Q learning algorithm, considering the average user performance of network, edge user performance and network environment interaction, the combined instantaneous reward and punishment value of fuzzy logic and Q learning algorithm is designed. The joint self-optimization of network capacity and coverage is carried out. Simulation results show that this method can achieve capacity and coverage self-optimization under intensive microcellular deployment, and effectively improve the average user throughput and edge user throughput of the system.
【作者单位】： 重庆邮电大学移动通信技术重点实验室;
【基金】：国家“863”计划资助项目(2014AA0101A701) 国家科技重大专项资助项目(2014ZX03003010-004) 国家自然科学基金资助项目(6157103)
【分类号】：TN929.5
【参考文献】
相关期刊论文 前2条
1 HAO Peng;YAN Xiao;Yu-Ngok Ruyue;YUAN Yifei;;Ultra Dense Network: Challenges, Enabling Technologies and New Trends[J];中国通信;2016年02期
2 丰雷;李文t，
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