基于机器学习的波束搜索算法设计  

A Beam Search Algorithm based on Machine Learning

作　　者：侯嘉智 梁晶[1] 刘高路 HOU Jia-zhi;LIANG Jing;LIU Gao-lu(School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)

机构地区：[1]重庆邮电大学通信与信息工程学院,重庆400065

出　　处：《光通信研究》2020年第4期62-67,共6页Study on Optical Communications

基　　金：国家科技重大专项资助项目(2018ZX03001026-002)。

摘　　要：在5G移动通信系统中,毫米波的应用可提供更大的带宽和更高的传输速率。5G毫米波基站通过大规模天线阵列发射高增益的定向窄波束以增加信号的覆盖范围。在毫米波基站密集部署场景中,用户需要搜索多个基站发出的大量窄波束来找到最优波束,该过程将消耗大量的时间和运算资源。文章设计了一种多基站场景下的系统模型,重点考虑了用户与基站的布局、大尺度衰落、波束定向增益以及毫米波信道的特性。在此基础上设计了一种基于机器学习的波束搜索算法,与传统穷举算法相比,该算法具有更低的延时和运算开销。In 5 G mobile communication systems, millimeter Wave(mmWave) can provide greater bandwidth and higher transmission rates. The mmWave base station can transmit a high gain directional narrow beam through a large-scale antenna array to increase signal coverage. When mmWave base station are densely deployed, users have to search a large number of narrow beams which sent by multiple base stations to find the optimal beam. This process can take a lot of time and computational resources. Therefore, this paper designs a system-level simulation model in a multi-base station scenario, focusing on the layout of users and base stations, large-scale fading, beam-orientation gain, and characteristics of mmWave channels. Finally, a beam search algorithm based on machine learning is designed. Compared with the traditional exhaustive algorithm, this algorithm has lower latency and computational overhead.

关 键 词：毫米波 机器学习 波束搜索 系统仿真 

分 类 号：TN928[电子电信—通信与信息系统]