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作 者:何世彪 李雪[2] 杨植景 廖勇[2] HE Shibiao;LI Xue;YANG Zhijing;LIAO Yong(School of Electronic Information,Chongqing Institute of Engineering,Chongqing 400056,P.R.China;School of Microelectronics and Communication Engineering,Chongqing University,Chongqing 400044,P.R.China)
机构地区:[1]重庆工程学院电子信息学院,重庆400056 [2]重庆大学微电子与通信工程学院,重庆400044
出 处:《重庆邮电大学学报(自然科学版)》2023年第4期584-595,共12页Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition)
基 金:重庆市自然科学基金项目(cstc2021jcyj-msxmX0941,cstc2019jcyj-msxmX0017)。
摘 要:近年来,高速移动通信受到业界的广泛关注,其中接收机侧的信道估计直接决定了系统的通信质量。正交频分复用(orthogonal frequency division multiplexing,OFDM)以其高带宽效率和抗多径衰落的特性,在目前的无线通信中被广泛采用。综述了高速移动场景下OFDM系统信道估计方法;描述了OFDM的系统模型;分析了高速移动场景下信道的时间/频率选择性衰落特性(双选特性)和稀疏特性。基于深度学习和压缩感知的信道估计方法,其高精度和低复杂度被用于高速移动场景,该文分别对基于深度学习和压缩感知的信道估计算法进行了归纳、对比、分析,探讨了高速移动场景下信道估计的发展趋势。In recent years,high-mobility communication has attracted wide attention from industry and academia.At the receiver,the channel estimation directly determines the communication quality of system.Orthogonal frequency division multiplexing(OFDM)is widely adopted in current wireless communication due to its high bandwidth efficiency and anti-multipath fading characteristics.Therefore,this paper summarizes the channel estimation methods for OFDM systems in high-mobility scenario.Firstly,the system model of OFDM is described.Then,based on the characteristics of wireless channel in high-mobility sceneries,the time/frequency selective fading(doubly selective fading)and the sparsity of the channel are analyzed.Secondly,channel estimation methods based on deep learning and compression sensing are used in high-mobility sceneries due to their high accuracy and low complexity,which are summarized and compared in this paper.Finally,in terms of channel estimation in high-mobility sceneries,we discuss the development trend and some directions to focus on.
关 键 词:正交频分复用(OFDM) 信道估计 高速移动 双选衰落 多普勒频移 稀疏特性 深度学习 压缩感知
分 类 号:TN911.72[电子电信—通信与信息系统]
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