摘要
Terahertz wireless communication has been regarded as an emerging technology to satisfy the ever-increasing demand of ultra-high-speed wireless communications.However,affected by the imperfections of cheap and energy-efficient Terahertz devices,Terahertz signals suffer from serve hybrid distortions,including in-phase/quadrature imbalance,phase noise and nonlinearity,which degrade the demodulation performance significantly.To improve the robustness against these hybrid distortions,an improved autoencoder is proposed,which includes coding the transmitted symbols at the transmitter and decoding the corresponding signals at the receiver.Moreover,due to the lack of information of Terahertz channel during the training of the autoencoder,a fitting network is proposed to approximate the characteristics of Terahertz channel,which provides an approximation of the gradients of loss.Simulation results show that our proposed autoencoder with fitting network can recover the transmitted symbols under serious hybrid distortions,and improves the demodulation performance significantly.
基金
supported in part by the National Natural Science Foundation of China(Grant 62101306)
in part by the National Key R&D Program of China(Grant 2018YFB1801501)
in part by Shenzhen Special Projects for the Development of Strategic Emerging Industries(201806081439290640)
in part by Shenzhen Wireless over VLC Technology Engineering Lab Promotion
in part by Postdoctoral Science Foundation of China(Grant 2020M670332)。
作者简介
Zhaohui Huang received the B.S.degree in electronic engineering from Tsinghua University in 2021.He is currently working towards the doctor’s degree in Tsinghua University.His current interests include Terahertz communication and deep learning;The corresponding author:Dongxuan He received the B.S.degree in Automation from Beijing institute of technology(BIT)in 2013,and received Ph.D.degree in Information and communication systems from BIT in 2019.From 2017 to 2018,he was a visiting student in Singapore university of technology and design(SUTD).He is currently a postdoctoral researcher in department of electronic engineering,Tsinghua University.His current interests include Terahertz communication,deep leaning,physical layer security.email:dongxuan he@mail.tsinghua.edu.cn;Jiaxuan Chen received the B.S.and Ph.D.degree from Tsinghua University,in 2016 and 2021 with the Department of Electronic Engineering,Tsinghua University.Her current research interests include wireless communications,signal processing,optical wireless communications,and vehicular ad-hoc networks;Zhaocheng Wang received the B.S.,M.S.,and Ph.D.degrees from Tsinghua University in 1991,1993,and 1996,respectively.From 1996 to 1997,he was a PostDoctoral Fellow with Nanyang Technological University,Singapore.From 1997 to 1999,he was a Research Engineer/Senior Engineer with OKI Techno Centre(Singapore)Pte.,Ltd.,Singapore.From 1999 to 2009,he was a Senior Engineer/Principal Engineer with Sony Deutschland GmbH,Germany.Since 2009,he has been a Professor with the Department of Electronic Engineering,Tsinghua University,where he is currently the Director of the Broadband Communication Key Laboratory,Beijing National Research Center for Information Science and Technology(BNRist)..His research interests include wireless communications,millimeter wave communications,and optical wireless communications;Sheng Chen received the B.Eng.degree in control engineering from the East China Petroleum Institute,Dongying,China,in 1982,and the Ph.D.degree in control engineering from the City,University of London,London,U.K.,in 1986.In 2005,he was awarded the higher doctoral degree,Doctor of Sciences(D.Sc.),from the University of Southampton,Southampton,U.K.From 1986 to 1999,he held research and academic appointments with the Universities of Sheffield,Edinburgh and Portsmouth,all in U.K.Since 1999,he has been with the School of Electronics and Computer Science,University of Southampton,where he holds the post of a Professor in intelligent systems and signal processing.His research interests include neural network and machine learning,wireless communications,and adaptive signal processing.
