基于电磁环境大数据的智能基站布设方法  

Intelligent base station layout method based on big data of electromagnetic environment

作　　者：陈昱帆 邵尉 于宝泉 刘瑾 钱祖平 黄启量 俞璐 CHEN Yufan;SHAO Wei;YU Baoquan;LIU Jin;QIAN Zuping;HUANG Qiliang;YU Lu(College of Communication Engineering,Army Engineering University of PLA,Nanjing Jiangsu 210007,China)

机构地区：[1]陆军工程大学通信工程学院,江苏南京210007

出　　处：《太赫兹科学与电子信息学报》2022年第1期47-52,共6页Journal of Terahertz Science and Electronic Information Technology

基　　金：江苏省自然科学基金资助项目(BK20160080)。

摘　　要：基站选址优化问题是移动通信中的研究热点,一个好的基站选址方案不仅能够节约资源,而且可以提高用户的通信体验。然而,基站布设常面对的是一个多参数、多约束、非线性的复杂问题,难以通过传统的优化方法进行求解。本文提出一种基于大数据的智能基站布设方法,根据实测电磁环境大数据构建基于深度学习的电波传播模型,使传播模型更加精确;采用空间自适应学习方法,在传播模型的基础上构建基站选址优化模型。通过在每次迭代过程中以较小概率选择性能较差的基站布设点,从而避免算法陷入局部最优。Base station location optimization is a research hotspot in mobile communication. A good base station location scheme can not only save resources, but also improve users’ communication experience. However, the base station layout is often faced with a complex problem of multi-parameter,multi-constraint and nonlinearity, which is difficult to be solved by traditional optimization methods. In this paper, an intelligent base station layout method based on big data is proposed. Firstly, the radio wave propagation model based on deep learning is built according to the measured big data of electromagnetic environment, which makes the propagation model more accurate. Then, the spatial adaptive learning method is utilized to construct the base station location optimization model on the basis of the propagation model. By selecting the base station placement points having poor performance with a small probability in each iteration process, the algorithm can avoid falling into local optimality. The experimental simulation results show that the proposed base station layout method has fast convergence speed, wide coverage rate and good user communication experience.

关 键 词：基站选址 深度学习 电波传播模型 空间自适应学习 

分 类 号：TN929.5[电子电信—通信与信息系统]