Deep learning based Doppler frequency offset estimation for 5G-NR downlink in HSR scenario  

作　　者：YANG Lihua WANG Zenghao ZHANG Jie JIANG Ting 杨丽花;WANG Zenghao;ZHANG Jie;JIANG Ting(College of Communication and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,P.R.China;College of Electronic and Information Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,P.R China)

机构地区：[1]College of Communication and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,P.R.China [2]College of Electronic and Information Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,P.R China

出　　处：《High Technology Letters》2022年第2期115-121,共7页高技术通讯（英文版）

基　　金：Supported by the National Science Foundation Program of Jiangsu Province(No.BK20191378);the National Science Research Project of Jiangsu Higher Education Institutions(No.18KJB510034);the 11th Batch of China Postdoctoral Science Fund Special Funding Project(No.2018T110530);the National Natural Science Foundation of China(No.61771255)。

摘　　要：In the fifth-generation new radio(5G-NR) high-speed railway(HSR) downlink,a deep learning(DL) based Doppler frequency offset(DFO) estimation scheme is proposed by using the back propagation neural network(BPNN).The proposed method mainly includes pre-training,training,and estimation phases,where the pre-training and training belong to the off-line stage,and the estimation is the online stage.To reduce the performance loss caused by the random initialization,the pre-training method is employed to acquire a desirable initialization,which is used as the initial parameters of the training phase.Moreover,the initial DFO estimation is used as input along with the received pilots to further improve the estimation accuracy.Different from the training phase,the initial DFO estimation in pre-training phase is obtained by the data and pilot symbols.Simulation results show that the mean squared error(MSE) performance of the proposed method is better than those of the available algorithms,and it has acceptable computational complexity.

关 键 词：fifth-generation new radio(5G-NR) high-speed railway(HSR) deep learning(DL) back propagation neural network(BPNN) Doppler frequency offset(DFO)estimation 

分 类 号：U285[交通运输工程—交通信息工程及控制] TP18[交通运输工程—道路与铁道工程] TN929.5[自动化与计算机技术—控制理论与控制工程]