基于深度学习的信道估计技术研究进展  

Research Progress of Channel Estimation Based on Deep Learning Technology

作　　者：廖勇[1] 李雪[1] 王幕熙 杨植景 周晨虹 LIAO Yong;LI Xue;WANG Muxi;YANG Zhijing;ZHOU Chenhong(School of Microelectronics and Communication Engineering,Chongqing University,Chongqing 400044,China;Military Representative Office in Guiyang,Military Representative Bureau of PLA Army Equipment Department in Chongqing,Guiyang 550006,China;Chongqing Jinmei Communication Co.,Ltd.,Chongqing 400030,China)

机构地区：[1]重庆大学微电子与通信工程学院,重庆400044 [2]中国人民解放军陆军装备部驻重庆地区军代局驻贵阳地区军事代表室,贵阳550006 [3]重庆金美通信有限责任公司,重庆400030

出　　处：《电讯技术》2023年第10期1642-1650,共9页Telecommunication Engineering

摘　　要：信道估计是接收机基带信号处理的关键,直接决定了无线通信系统的通信服务质量。传统的信道估计方法已经不能满足日益复杂和个性化的现代通信需求,同时人工智能技术特别是深度学习已被应用于无线通信物理层并带来了良好的通信性能增益。为系统地总结上述研究成果,并探讨未来的技术发展趋势,从数据驱动和模型驱动两方面分别对基于深度学习的信道估计方法进行了分析和归纳,并且描述了其中代表性算法,最后探讨了基于深度学习的信道估计的研究挑战与趋势。Channel estimation is the key to baseband signal processing of receiver,which directly determines the communication quality of a wireless communication system.Traditional channel estimation methods cannot meet the increasingly complex and personalized needs of modern communication.Moreover,artificial intelligence technology,especially deep learning,has been applied to the physical layer of wireless communication and has brought good communication performance gains.In order to systematically summarize above research results and discuss the future technology development trend,the authors analyze and summarize the channel estimation methods based on deep learning from data-driven and model-driven aspects,and introduce the representative algorithms in detail.Finally,the research challenges and trends of channel estimation based on deep learning are discussed.

关 键 词：无线通信 信道估计 深度学习 数据驱动 模型驱动 

分 类 号：TN929.5[电子电信—通信与信息系统]