Research On Dynamic Beamforming Technology Of High-speed Railway Distributed Antenna System

With the advent of 5G communication,people have higher and higher requirements for network data transmission rate.Large-scale distributed antenna systems have attracted much attention due to their high data transmission rate,high reliability and scalability.Previous studies on distributed antenna systems have focused on the scenes where the users are not moving or moving slowly.There are few studies on high-speed scenes with distributed antenna systems,such as high-speed railway.This dissertation focuses on the characteristics of large-scale distributed antenna systems and we discuss the problems the existing technical problems of distributed antenna systems in high-speed railway systems.Firstly,we introduce the research background and current situation of distributed antenna systems,then describes the structure of distributed antenna systems.Furthermore,we analyze the field strength characteristics in distributed antenna systems and beamforming performance based on basic wireless channel characteristics and models in distributed antenna systems.Finally,the impact on the performance of distributed antenna systems in mobile environment is studied,which is caused by the channel correlation related with user motion,motion speed,channel state information feedback and carrier frequency.Secondly,based on the analysis of the main characteristics of the distributed antenna system,the application and implementation method of dynamic beamforming in high-speed rail are studied.Firstly,the design of distributed antenna system in high-speed rail scene is studied,including the antenna spacing design and the distance between the antenna array and the railway.A dynamic beamforming scheme is proposed for high-speed train operation,including dynamic beam generation and beam switching.Besides,the system performance of dynamic beamforming in ideal or random fading scenarios is analyzed under the two situations in which the distributed antenna deployment position is with or without deviation.Finally,in order to mitigate the impact on the performance of dynamic beamforming caused by the antenna position deviation,a method based on machine learning and deep learning to estimate position deviation is proposed.We first introduce three algorithmic principles for dealing with regression problems,namely support vector regression,extreme gradient boosting,and deep neural networks.Then the problem is modeled,and the data acquisition and feature extraction process when the method is implemented is analyzed in detail.Finally,the reasonable parameters of each model are set for training estimation,and a new composite model is constructed for deviation estimation.It shows that this method can effectively estimate the positional deviation in most cases,which provides a convenient means for signal compensation of large-scale distributed antenna systems in high-speed railway communication.