Research On Channel Tracking Algorithm In Millimeter Wave Communication Systems

Facing the ever-increasing demand for wireless communication,millimeter wave communication has become an important technology to support the high-speed wireless communication with its rich spectrum resources.At the same time,with the further deployment of 5G and the emerging industries such as smart cities and autonomous driving brought by 6G networks,millimeter wave communication will make it the best way to promote the development of new services owing to the features of low latency and large bandwidth.However,due to the characteristics of high frequency and short wavelength,millimeter wave communication exhibits shortcomings such as severe channel attenuation and shortened transmission distance.In order to solve this problem,millimeter wave communication systems usually use highly directional beamforming technology to provide transmit and receive gains to overcome the large path loss problem encountered in the millimeter wave band.The millimeter wave communication system can design accurate beamforming by training beams.This method of training beams is practical in static or relatively slow-changing environments.But in fast-changing environments(such as vehicle environments),it will become unacceptable.Therefore,it is very important that the millimeter wave mobile communications design channel tracking algorithms to reduce the overhead caused by frequent beam training.This paper aims to study the key channel tracking technology in millimeter wave communication.Firstly,this paper realizes the tracking algorithms of millimeter wave communication system.Then,this paper works on tracking millimeter wave channels.Finally,this paper designs coordinated channel tracking for multi-base station millimeter wave networks.A comprehensive and specific channel tracking study was conducted,and the performance verification was carried out.This paper’s contributions can be summarized as follows:(1)First,this paper summarizes the traditional millimeter wave channel tracking method,improves it according to the characteristics of the millimeter wave communication system,and realizes two improved Kalman filter channel tracking algorithms.Aiming at the structural characteristics of complex millimeter wave networks,a new channel tracking algorithm that combines machine learning and Kalman filtering is proposed,and its performance is better than traditional algorithms.(2)Considering the huge overhead caused by frequent channel estimation in millimeter wave communication,the relationship between the received signal of the millimeter wave system and channel tracking is studied,and a machine learning model that uses past time information to help predict is designed.Designed for different millimeter wave scenarios,a model that focuses on channel state information is proposed.(3)Finally,this paper considers a more complex multi-base station millimeter-wave vehicle network.This communication network scenario puts forward higher requirements for the channel tracking algorithm due to the faster terminal movement speed and frequent base station switching.Aiming at the base station handover that may occur during the movement of vehicle users,a collaborative channel tracking algorithm based on machine learning deployed in the cloud is designed,and simulation tests have been carried out.The research results of this paper can not only promote the development of millimeter wave communications,enhance the support of millimeter wave wireless communication networks for mobile terminals,but also provide technical support for the research,practicality and standard formulation of next-generation millimeter wave mobile networks.