摘要
随着无线通信应用边界的不断扩展,无线通信应用环境也日趋复杂多样,面临射频损伤、信道衰落、干扰和噪声等负面影响,给接收端恢复原始信息带来挑战。借鉴深度学习方法在计算机视觉、模式识别、自然语言处理等领域取得的研究成果,基于深度学习的无线通信接收技术受到学术界和产业界的广泛关注。首先阐述了国内外基于深度学习无线通信接收技术的研究现状;接着概述了信号大数据背景下无线通信接收所面临的技术挑战,并提出基于深度神经网络的无线通信智能接收参考架构;最后探讨了信号大数据背景下无线通信智能接收方法的发展趋势。为基于深度学习无线通信技术的研究和发展提供借鉴。
With the continues expansion of the application boundary for wireless communications,the application environment of wireless communications is becoming increasingly complex and diverse,which faces negative im-pacts such as radio frequency(RF)damage,channel fading,interference and noise.It brings difficulties to recover the original information at the receiver.Drawing from the research results of deep learning methods in computer vision,pattern recognition,natural language processing and other fields,wireless communication reception technology based on deep learning has received wide attentions from both academia and industry.Firstly,the current research status of wireless communication reception technology based on deep learning at home and abroad was described.Secondly,the current technical challenges of wireless communication reception in the context of signal big data were outlined,and a reference architecture of intelligent wireless communication reception based on deep neural network was proposed.Finally,the development trend of intelligent wireless communication reception method in the context of signal big data was discussed.It is expected to provide reference for the research and development of wireless communication technology based on deep learning.
作者
李攀攀
谢正霞
乐光学
刘鑫
LI Panpan;XIE Zhengxia;YUE Guangxue;LIU Xin(College of Information Science and Technology,Jiaxing University,Jiaxing 314001,China;College of Civil Engineering and Architecture,Jiaxing University,Jiaxing 314001,China;School of Information and Communication Engineering,Dalian University of Technology,Dalian 116024,China)
出处
《电信科学》
2022年第2期1-17,共17页
Telecommunications Science
基金
国家自然科学基金资助项目(No.U19B2015,No.U1833102)。
关键词
无线通信
信号大数据
深度学习
深度神经网络
信号接收
wireless communication
signal big data
deep learning
deep neural network
signal reception
