摘要
为了解决车联网毫米波通信中的波束对齐问题,结合全景交通上下文编码方式,提出了一种基于机器学习的波束预测方法。该方法通过模拟双车道城市峡谷环境,获取大量环境信息,并进行全景交通上下文环境编码,基于该编码信息,运用机器学习模型对波束进行训练,获得预测波束。仿真结果表明,相较于传统波束选择方法,提出的波束对齐方法能够更有效地利用环境信息,提高动态场景下毫米波通信的波束预测精度和鲁棒性。
To address the issue of beam alignment in vehicular millimeter-wave communications,this paper proposes a machine learning-based beam prediction method incorporating panoramic traffic context coding.By simulating a dual-lane urban canyon environment,a large amount of environmental data are collected and encoded into panoramic traffic context information.Based on the coded information,a machine learning model is employed to train and predict the optimal beams.Simulation results demonstrate that,compared to traditional beam selection methods,the proposed beam alignment approach leverages environmental information more effectively,improving the accuracy and robustness of beam prediction of millimeter-wave communication in dynamic scenarios.
作者
靳昊文
仲伟志
刘响
王文捷
林志鹏
JIN Haowen;ZHONG Weizhi;LIU Xiang;WANG Wenjie;LIN Zhipeng(College of astronautics,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China;College of electronic and information engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)
出处
《移动通信》
2024年第12期39-45,75,共8页
Mobile Communications
基金
江苏省重点研发计划(产业前瞻与关键核心技术)“6G普适无线信道建模理论方法与性能关键技术研发”(BE2022067,BE2022067-1,BE2022067-3)。
关键词
车联网
毫米波通信
波束预测
全景交通上下文编码
机器学习
vehicle networking
millimeter wave communication
beam prediction
panoramic traffic context coding
machine learning
