Deep Learning-Assisted OFDM Detection with Hardware Impairments  

作　　者：Amit Singh Sanjeev Sharma Kuntal Deka Vimal Bhatia 

机构地区：[1]Indian Institute of Technology(IIT)BHU Varanasi,Varanasi 221005,India [2]Indian Institute of Technology(IIT)Guwahati,Guwahati 781039,India [3]Indian Institute of Technology(IIT)Indore,Indore 452020,India

出　　处：《Journal of Communications and Information Networks》2023年第4期378-388,共11页通信与信息网络学报（英文）

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.

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 system.In 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)estimators.Thus,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 techniques.Further,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 order.Results show that there are no significant effects of HIs on channel estimation and signal detection in the proposed DL-assisted algorithm.The proposed DL-assisted detection improves the OFDM performance as compared to the conventional LS/MMSE under severe Hls.

关 键 词：OFDM DL HIS channel estimation signal detection 

分 类 号：TN92[电子电信—通信与信息系统]