Deep Learning-Assisted OFDM Detection with Hardware Impairments

作     者：Amit Singh Sanjeev Sharma Kuntal Deka Vimal Bhatia 

作者机构：Indian Institute of Technology(IIT)BHU VaranasiVaranasi 221005India Indian Institute of Technology(IIT)GuwahatiGuwahati 781039India Indian Institute of Technology(IIT)IndoreIndore 452020India 

出 版 物：《Journal of Communications and Information Networks》 (通信与信息网络学报（英文）)

年 卷 期：2023年第8卷第4期

页      面：378-388页

核心收录：

学科分类：080904[工学-电磁场与微波技术] 0810[工学-信息与通信工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 081001[工学-通信与信息系统] 

基　　金：supported by the Ministry of Science and Technology SERB under Grant SRG/2021/000199 and by the Indian National Academy of Engineering(INAE)Project with Sanction under Grant 2023/INTW/10 

主　　题：OFDM DL HIs channel estimation signal detection 

摘      要：This paper introduces a deep learning(DL)algorithm for estimating doubly-selective fading channel and detecting signals in orthogonal frequency division multiplexing(OFDM)communication systems affected by hardware impairments(HIs).In practice,hardware imperfections are present at the transceivers,which are modeled as direct current(DC)offset,carrier frequency offset(CFO),and in-phase and quadrature-phase(IQ)imbalance at the transmitter and the receiver in OFDM *** HIs,the explicit system model could not be mathematically derived,which limits the performance of conventional least square(LS)or minimum mean square error(MMSE)***,we consider time-frequency response of a channel as a 2D image,and unknown values of the channel response are derived using known values at the pilot locations with DL-based image super-resolution,and image restoration ***,a deep neural network(DNN)is designed to fit the mapping between the received signal and transmit symbols,where the number of outputs equals to the size of the modulation *** show that there are no significant effects of HIs on channel estimation and signal detection in the proposed DL-assisted *** proposed DL-assisted detection improves the OFDM performance as compared to the conventional LS/MMSE under severe Hls.