Two-Stage LMMSE/DNN Receiver for High-Order Modulation

被引:0
作者
Huang, Zhaohui [1 ]
He, Dongxuan [2 ]
Wang, Zhaocheng [1 ]
机构
[1] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China
[2] Beijing Inst Technol, Sch Informat & Elect, Beijing 100081, Peoples R China
关键词
High order modulation; deep learning; signal demodulation; nonlinear distortion;
D O I
10.1109/LCOMM.2023.3281464
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
High-order modulation has been seen as a promising technology to increase the spectral efficiency of wireless communications. Unfortunately, higher-order signals are susceptible to power amplifier (PA) nonlinearities and multi-path effects of the channel, leading to nonlinear distortion and severe inter-symbol interference. To guarantee the reliable detection of high-order signals, a two-stage receiver consisting of linear minimize mean squared error (LMMSE) equalizer and deep neural network (DNN) demodulator, namely TSLD receiver, is proposed, where LMMSE equalizer and DNN are deployed to eliminate inter-symbol interference and handle the amplifier nonlinearity, respectively. To facilitate the implementation of our proposed receiver, the specific pilot structure is designed, where low-order modulations are used for LMMSE equalizer and high-order modulations are used for DNN training. Simulation results show that our proposed TSLD receiver for high-order demodulation could recover the transmitted symbols effectively, even under serious multi-path and nonlinerity scenarios.
引用
收藏
页码:2068 / 2072
页数:5
相关论文
共 13 条
[1]  
[Anonymous], 2008, RF imperfections in high-rate wireless systems: Impact and digital compensation
[2]  
Balasubramanian R, 2004, IEEE MILIT COMMUN C, P1028
[3]   Reconfigurable Intelligent Surface (RIS)-Aided Vehicular Networks Their Protocols, Resource Allocation, and Performance [J].
Chen, Yuanbin ;
Wang, Ying ;
Zhang, Jiayi ;
Zhang, Ping ;
Hanzo, Lajos .
IEEE VEHICULAR TECHNOLOGY MAGAZINE, 2022, 17 (02) :26-36
[4]   Satellite Data Transmission Method for Deep Learning-Based AutoEncoders [J].
Fan, YiLe ;
Li, YuanPeng ;
Chai, TianYi ;
Ding, Dan .
2021 INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGIES FOR DISASTER MANAGEMENT (ICT-DM), 2021, :38-42
[5]  
Fang LT, 2017, 2017 IEEE 2ND INTERNATIONAL CONFERENCE ON OPTO-ELECTRONIC INFORMATION PROCESSING (ICOIP), P16, DOI 10.1109/OPTIP.2017.8030690
[6]  
Feng XX, 2019, PROCEEDINGS OF 2019 IEEE 8TH JOINT INTERNATIONAL INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE CONFERENCE (ITAIC 2019), P1647, DOI [10.1109/ITAIC.2019.8785812, 10.1109/itaic.2019.8785812]
[7]  
Goldsmith A., 2005, Wireless Communications, DOI 10.1017/CBO9780511841224
[8]  
King DB, 2015, ACS SYM SER, V1214, P1, DOI 10.1021/bk-2015-1214.ch001
[9]  
Lai H., 1999, Gateway to 21st Century Communications Village. VTC 1999-Fall. IEEE VTS 50th Vehicular Technology Conference (Cat. No.99CH36324), P1501, DOI 10.1109/VETECF.1999.801544
[10]   Deep Learning Methods for Improved Decoding of Linear Codes [J].
Nachmani, Eliya ;
Marciano, Elad ;
Lugosch, Loren ;
Gross, Warren J. ;
Burshtein, David ;
Be'ery, Yair .
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, 2018, 12 (01) :119-131