Research progress and trends of deep learning based wireless communication receiving method

Panpan LI; Zhengxia XIE; Guangxue YUE; Xin LIU

doi:10.11959/j.issn.1000-0801.2022025

您当前的位置：

首页 >

文章列表页 >

Research progress and trends of deep learning based wireless communication receiving method

Reviews | 更新时间：2024-06-05

- Research progress and trends of deep learning based wireless communication receiving method
- Telecommunications Science Vol. 38, Issue 2, Pages: 1-17(2022)
- 作者机构：
  
  1. 嘉兴学院信息科学与工程学院，浙江嘉兴 314001
  2. 嘉兴学院建筑工程学院，浙江嘉兴 314001
  3. 大连理工大学信息与通信工程学院，辽宁大连 116024
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2022025
  CLC： TN929.5
- Published Online：2022-02，
  
  Published：20 February 2022
- 稿件说明：
移动端阅览
Panpan LI, Zhengxia XIE, Guangxue YUE, et al. Research progress and trends of deep learning based wireless communication receiving method[J]. Telecommunications science, 2022, 38(2): 1-17.
DOI：

Panpan LI, Zhengxia XIE, Guangxue YUE, et al. Research progress and trends of deep learning based wireless communication receiving method[J]. Telecommunications science, 2022, 38(2): 1-17. DOI： 10.11959/j.issn.1000-0801.2022025.

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。

Abstract

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 impacts 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.

关键词

Keywords

references

RESTUCCIA F , MELODIA T . Physical-layer deep learning:challenges and applications to 5G and beyond [J ] . arXiv preprint arXiv:2004.10113 , 2020 .

谢维信 , 陈曾平 , 裴继红 , 等 . 大数据背景下的信号处理 [J ] . 中国科学:信息科学 , 2013 , 43 ( 12 ): 1525 - 1546 .

XIE W X , CHEN Z P , PEI J H , et al . Signal processing in the context of big data [J ] . Scientia Sinica (Informationis) , 2013 , 43 ( 12 ): 1525 - 1546 .

章坚武 , 王路鑫 , 孙玲芬 , 等 . 人工智能在5G系统中的应用综述 [J ] . 电信科学 , 2021 , 37 ( 5 ): 14 - 31 .

ZHANG J W , WANG L X , SUN L F , et al . An survey on application of artificial intelligence in 5G system [J ] . Telecommunications Science , 2021 , 37 ( 5 ): 14 - 31 .

夏明华 , 朱又敏 , 陈二虎 , 等 . 海洋通信的发展现状与时代挑战 [J ] . 中国科学:信息科学 , 2017 , 47 ( 6 ): 677 - 695 .

XIA M H , ZHU Y M , CHEN E H , et al . The state of the art and challenges of marine communications [J ] . Scientia Sinica (Informationis) , 2017 , 47 ( 6 ): 677 - 695 .

WANG T Q , WEN C K , WANG H Q , et al . Deep learning for wireless physical layer:opportunities and challenges [J ] . China Communications , 2017 , 14 ( 11 ): 92 - 111 .

WANG F X , ZHANG M , WANG X X , et al . Deep learning for edge computing applications:a state-of-the-art survey [J ] . IEEE Access , 2020 ( 8 ): 58322 - 58336 .

尤肖虎 , 张川 , 谈晓思 , 等 . 基于AI的5G技术:研究方向与范例 [J ] . 中国科学:信息科学 , 2018 , 48 ( 12 ): 1589 - 1602 .

YOU X H , ZHANG C , TAN X S , et al . AI for 5G:research directions and paradigms [J ] . Scientia Sinica (Informationis) , 2018 , 48 ( 12 ): 1589 - 1602 .

SALH A , AUDAH L , SHAH N S M , et al . A survey on deep learning for ultra-reliable and low-latency communications challenges on 6G wireless systems [J ] . IEEE Access , 2021 ( 9 ): 55098 - 55131 .

MAO Q , HU F , HAO Q . Deep learning for intelligent wireless networks:a comprehensive survey [J ] . IEEE Communications Surveys ＆ Tutorials , 2018 , 20 ( 4 ): 2595 - 2621 .

张静 , 金石 , 温朝凯 , 等 . 基于人工智能的无线传输技术最新研究进展 [J ] . 电信科学 , 2018 , 34 ( 8 ): 46 - 55 .

ZHANG J , JIN S , WEN C K , et al . An overview of wireless transmission technology utilizing artificial intelligence [J ] . Telecommunications Science , 2018 , 34 ( 8 ): 46 - 55 .

HEH T , JIN S , WENC K , et al . Model-driven deep learning for physical layer communications [J ] . IEEE Wireless Communications , 2019 , 26 ( 5 ): 77 - 83 .

QIN Z J , YE H , LI G Y , et al . Deep learning in physical layer communications [J ] . IEEE Wireless Communications , 2019 , 26 ( 2 ): 93 - 99 .

LEE W , JO O , KIM M . Application of end-to-end deep learning in wireless communications systems [J ] . arXiv preprint arXiv:1808.02394 , 2018 .

彭德义 . 基于机器学习的 5G无线通信物理层关键技术研究 [J ] . 湖南工程学院学报(自然科学版) , 2019 , 29 ( 3 ): 37 - 43 .

PENG D Y . Research for key technology of 5G wireless network PHY based on machine learning [J ] . Journal of Hunan Institute of Engineering (Natural Science Edition) , 2019 , 29 ( 3 ): 37 - 43 .

高向堃 . 基于深度学习的物理层无线通信技术机遇与挑战 [J ] . 中国新通信 , 2019 , 21 ( 14 ): 96 .

GAO X K . Opportunities and challenges of deep learning based physical layer wireless communication techniques [J ] . China New Telecommunications , 2019 , 21 ( 14 ): 96 .

ZHANG C Y , PATRAS P , HADDADI H . Deep learning in mobile and wireless networking:a survey [J ] . IEEE Communications Surveys ＆ Tutorials , 2019 , 21 ( 3 ): 2224 - 2287 .

ZHENG S L , CHEN S C , YANG X N . DeepReceiver:a deep learning-based intelligent receiver for wireless communications in the physical layer [J ] . IEEE Transactions on Cognitive Communications and Networking , 2020 , 7 ( 1 ): 5 - 20 .

刘留 , 张建华 , 樊圆圆 , 等 . 机器学习在信道建模中的应用综述 [J ] . 通信学报 , 2021 , 42 ( 2 ): 134 - 153 .

LIU L , ZHANG J H , FAN Y Y , et al . Survey of application of machine learning in wireless channel modeling [J ] . Journal on Communications , 2021 , 42 ( 2 ): 134 - 153 .

王承祥 , 黄杰 , 王海明 , 等 . 面向6G的无线通信信道特性分析与建模 [J ] . 物联网学报 , 2020 , 4 ( 1 ): 19 - 32 .

WANG C X , HUANG J , WANG H M , et al . 6G oriented wireless communication channel characteristics analysis and modeling [J ] . Chinese Journal on Internet of Things , 2020 , 4 ( 1 ): 19 - 32 .

NEUMANN D , WIESE T , UTSCHICK W . Learning the MMSE channel estimator [J ] . IEEE Transactions on Signal Processing , 2018 , 66 ( 11 ): 2905 - 2917 .

GIZZINI A K , CHAFII M , NIMR A , et al . Adaptive channel estimation based on deep learning [C ] // Proceedings of 2020 IEEE 92nd Vehicular Technology Conference . Piscataway:IEEE Press , 2020 : 1 - 5 .

BAI Q B , WANG J T , ZHANG Y , et al . Deep learning-based channel estimation algorithm over time selective fading channels [J ] . IEEE Transactions on Cognitive Communications and Networking , 2020 , 6 ( 1 ): 125 - 134 .

YANG Y W , GAO F F , MA X L , et al . Deep learning-based channel estimation for doubly selective fading channels [J ] . IEEE Access , 2019 ( 7 ): 36579 - 36589 .

SOLTANI M , POURAHMADI V , MIRZAEI A , et al . Deep learning-based channel estimation [J ] . IEEE Communications Letters , 2019 , 23 ( 4 ): 652 - 655 .

MAO H X , LU H C , LU Y J , et al . RoemNet:robust meta learning based channel estimation in OFDM systems [C ] // Proceedings of ICC 2019 - 2019 IEEE International Conference on Communications . Piscataway:IEEE Press , 2019 : 1 - 6 .

廖勇 , 花远肖 , 姚海梅 . 基于深度学习的OFDM信道估计 [J ] . 重庆邮电大学学报(自然科学版) , 2019 , 31 ( 3 ): 348 - 353 .

LIAO Y , HUA Y X , YAO H M . Channel estimation based on deep learning for OFDM systems [J ] . Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition) , 2019 , 31 ( 3 ): 348 - 353 .

O’SHEA T J , ROY T , WEST N . Approximating the void:learning stochastic channel models from observation with variational generative adversarial networks [C ] // Proceedings of 2019 International Conference on Computing,Networking and Communications (ICNC) . Piscataway:IEEE Press , 2019 : 681 - 686 .

ZAMANIPOUR M . A survey on deep-learning based techniques for modeling and estimation of massive MIMO channels [J ] . arXiv preprint arXiv:1910.03390 , 2019 .

HUANG H J , YANG J ， HUANGH , et al . Deep learning for super-resolution channel estimation and DOA estimation based massive MIMO system [J ] . IEEE Transactions on Vehicular Technology , 2018 , 67 ( 9 ): 8549 - 8560 .

MEHRABI M , MOHAMMADKARIMI M , ARDAKANI M , et al . Decision directed channel estimation based on deep neural network k-step predictor for MIMO communications in 5G [J ] . IEEE Journal on Selected Areas in Communications , 2019 , 37 ( 11 ): 2443 - 2456 .

廖勇 , 花远肖 , 姚海梅 , 等 . 高速移动环境下基于深度学习的信道估计方法 [J ] . 电子学报 , 2019 , 47 ( 8 ): 1701 - 1707 .

LIAO Y , HUA Y X , YAO H M , et al . Channel estimation method based on deep learning in high-speed mobile environments [J ] . Acta Electronica Sinica , 2019 , 47 ( 8 ): 1701 - 1707 .

METZLER C , MOUSAVI A , BARANIUK R . Learned D-AMP:principled neural network based compressive image recovery [C ] // Proceedings of the 31st International Conference on Neural Information Processing Systems 2017 . New York:ACM Press , 2017 : 1772 - 1783 .

HE H T , WEN C K , JIN S , et al . Deep learning-based channel estimation for beamspace mmWave massive MIMO systems [J ] . IEEE Wireless Communications Letters , 2018 , 7 ( 5 ): 852 - 855 .

ZHANG K , ZUO W M , CHEN Y J , et al . Beyond a Gaussian denoiser:residual learning of deep CNN for image denoising [J ] . IEEE Transactions on Image Processing , 2017 , 26 ( 7 ): 3142 - 3155 .

YE H Y , GAO FF , QIAN J , et al . Deep learning-based denoise network for CSI feedback in FDD massive MIMO systems [J ] . IEEE Communications Letters , 2020 , 24 ( 8 ): 1742 - 1746 .

GUO J J , WEN C K , JIN S . Deep learning-based CSI feedback for beamforming in single- and multi-cell massive MIMO systems [J ] . IEEE Journal on Selected Areas in Communications , 2021 , 39 ( 7 ): 1872 - 1884 .

SUN YY , XU W , FAN L S , et al . AnciNet:an efficient deep learning approach for feedback compression of estimated CSI in massive MIMO systems [J ] . IEEE Wireless Communications Letters , 2020 , 9 ( 12 ): 2192 - 2196 .

LU Z L , WANG J T , SONG J . Binary neural network aided CSI feedback in massive MIMO system [J ] . IEEE Wireless Communications Letters , 2021 , 10 ( 6 ): 1305 - 1308 .

SUN Y Y , XU W , LIANG L , et al . A lightweight deep network for efficient CSI feedback in massive MIMO systems [J ] . IEEE Wireless Communications Letters , 2021 , 10 ( 8 ): 1840 - 1844 .

CHEN S , GIBSON G J , COWAN C F N , et al . Adaptive equalization of finite non-linear channels using multilayer perceptrons [J ] . Signal Processing , 1990 , 20 ( 2 ): 107 - 119 .

PATRA J C , PAL R N . A functional link artificial neural network for adaptive channel equalization [J ] . Signal Processing , 1995 , 43 ( 2 ): 181 - 195 .

CHANG P R , WANG B C . Adaptive decision feedback equalization for digital satellitechannels using multilayer neural networks [J ] . IEEE Journal on Selected Areas in Communications , 1995 , 13 ( 2 ): 316 - 324 .

PATRA J C , POH W B , CHAUDHARI N S , et al . Nonlinear channel equalization with QAM signal using Chebyshev artificial neural network [C ] // Proceedings of 2005 IEEE International Joint Conference on Neural Networks . Piscataway:IEEE Press , 2005 : 3214 - 3219 .

KLAUTAU A , GONZÁLEZ-PRELCIC N , MEZGHANI A , et al . Detection and channel equalization with deep learning for low resolution MIMO systems [C ] // Proceedings of 2018 52nd Asilomar Conference on Signals,Systems,and Computers . Piscataway:IEEE Press , 2018 : 1836 - 1840 .

CARRERA D F , VARGAS-ROSALES C , YUNGAICELANAULA N M , et al . Comparative study of artificial neural network based channel equalization methods for mmWave communications [J ] . IEEE Access , 2021 ( 9 ): 41678 - 41687 .

杨旸 , 李扬 , 周明拓 . 基于数据驱动深度学习方法的无线信道均衡 [J ] . 中兴通讯技术 , 2018 , 24 ( 2 ): 25 - 29 .

YANG Y , LI Y , ZHOU M T . The wireless channel equalization based on data-driven deep learning method [J ] . ZTE Technology Journal , 2018 , 24 ( 2 ): 25 - 29 .

KUMARI S , SRINIVAS K K , KUMAR P . Channel and carrier frequency offset equalization for OFDM based UAV communications using deep learning [J ] . IEEE Communications Letters , 2020 , 25 ( 3 ): 850 - 853 .

FANG L T , WU L N . Deep learning detection method for signal demodulation in short range multipath channel [C ] // Proceedings of 2017 IEEE 2nd International Conference on Opto-Electronic Information Processing . Piscataway:IEEE Press , 2017 : 16 - 20 .

OUYANG X C , WU L N . Faster-than-Nyquist rate communication via convolutional neural networks-based demodulators [J ] . Journal of Southeast University (English Edition) , 2016 , 32 ( 1 ): 6 - 10 .

MOHAMMAD A S , REDDY N , JAMES F , et al . Demodulation of faded wireless signals using deep convolutional neural networks [C ] // Proceedings of 2018 IEEE 8th Annual Computing and Communication Workshop and Conference . Piscataway:IEEE Press , 2018 : 969 - 975 .

QI P H , ZHOU X Y , ZHENG S L , et al . Automatic modulation classification based on deep residual networks with multimodal information [J ] . IEEE Transactions on Cognitive Communications and Networking , 2020 , 7 ( 1 ): 21 - 33 .

WANG Y , GUI J , YIN Y , et al . Automatic modulation classification for MIMO systems via deep learning and zero-forcing equalization [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 5 ): 5688 - 5692 .

黄媛媛 , 张剑 , 周兴建 , 等 . 应用深度学习的信号解调 [J ] . 电讯技术 , 2017 , 57 ( 7 ): 741 - 744 .

HUANG Y Y , ZHANG J , ZHOU X J , et al . Demodulation with deep learning [J ] . Telecommunication Engineering , 2017 , 57 ( 7 ): 741 - 744 .

杨耀栋 , 吴乐南 . 相移键控信号的深度神经网络解调器 [J ] . 电子测量与仪器学报 , 2018 , 32 ( 4 ): 144 - 150 .

YANG Y D , WU L N . Realization of SSAE demodulator of MPPSK communication system [J ] . Journal of Electronic Measurement and Instrumentation , 2018 , 32 ( 4 ): 144 - 150 .

JI X F , WANG J , LI Y , et al . Modulation recognition in maritime multipath channels:a blind equalization-aided deep learning approach [J ] . China Communications , 2020 , 17 ( 3 ): 12 - 25 .

HANNA S , DICK C , CABRIC D . Signal processing based deep learning for blind symbol decoding and modulation classification [J ] . arXiv preprint arXiv:2106.10543 , 2021 .

SHENTAL O , HOYDIS J . “Machine LLRning”:learning to softly demodulate [J ] . arXiv preprint arXiv:1907.01512 , 2019 .

PENG S L , JIANG H Y , WANG H X , et al . Modulation classification based on signal constellation diagrams and deep learning [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2018 , 30 ( 3 ): 718 - 727 .

KRIZHEVSKY A , SUTSKEVER I , HINTON G E . Imagenetclassification with deep convolutional neural networks [C ] // Advances in Neural Information Processing Systems .[S.l.:s.n. ] , 2012 : 1097 - 1105 .

SZEGEDY C , LIU W , JIA Y Q , et al . Going deeper with convolutions [C ] // Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2015 : 1 - 9 .

陈铮 , 彭盛亮 , 李焕焕 , 等 . 基于深度学习与软件无线电的盲解调系统研究 [J ] . 信号处理 , 2019 , 35 ( 4 ): 649 - 655 .

CHEN Z , PENG S L , LI H H , et al . Research on blind demodulation system based on deep learning and software defined radio [J ] . Journal of Signal Processing , 2019 , 35 ( 4 ): 649 - 655 .

CHEN J Y , CUI H , MIAO S H , et al . FEM:Feature extraction and mapping for radio modulation classification [J ] . Physical Communication , 2021 , 45 : 101279 .

PEREZ-CRUZ F , MURILLO-FUENTES J J , CARO S . Nonlinear channel equalization with Gaussian processes for regression [J ] . IEEE Transactions on Signal Processing , 2008 , 56 ( 10 ): 5283 - 5286 .

OLMOS P M , MURILLO-FUENTES J J , PEREZ-CRUZ F , . Joint nonlinear channel equalization and soft LDPC decoding with Gaussian processes [J ] . IEEE Transactions on Signal Processing , 2009 , 58 ( 3 ): 1183 - 1192 .

GRUBER T , CAMMERER S , HOYDIS J , et al . On deep learning-based channel decoding [C ] // Proceedings of 2017 51st Annual Conference on Information Sciences and Systems (CISS) . Piscataway:IEEE Press , 2017 : 1 - 6 .

NACHMANI E , BACHAR Y , MARCIANO E , et al . Nearmaximum likelihood decoding with deep learning [J ] . arXiv preprint arXiv:1801.02726 , 2018 .

NACHMANI E , BE'ERY Y , BURSHTEIN D . Learning to decode linear codes using deep learning [C ] // Proceedings of 2016 54th Annual Allerton Conference on Communication,Control,and Computing (Allerton) . Piscataway:IEEE Press , 2016 : 341 - 346 .

LIANG F , SHEN C , WU F . An iterative BP-CNN architecture for channel decoding [J ] . IEEE Journal of Selected Topics in Signal Processing , 2018 , 12 ( 1 ): 144 - 159 .

潘睿 , 袁磊 . 脉冲信道下基于深度学习的BP译码方法 [J ] . 系统工程与电子技术 , 2020 , 42 ( 9 ): 2116 - 2122 .

PAN R , YUAN L . BP decoding method based on deep learning in impulsive channels [J ] . Systems Engineering and Electronics , 2020 , 42 ( 9 ): 2116 - 2122 .

DENG C H , YUANS B L . Reduced-complexity deep neural network-aided channel code decoder:a case study for BCH decoder [C ] // Proceedings of ICASSP 2019-2019 IEEE International Conference on Acoustics,Speech and Signal Processing . Piscataway:IEEE Press , 2019 : 1468 - 1472 .

LUGOSCH L , GROSS W J . Neural offset min-sum decoding [C ] // Proceedings of 2017 IEEE International Symposium on Information Theory . Piscataway:IEEE Press , 2017 : 1361 - 1365 .

NACHMANI E , MARCIANO E , LUGOSCH L , et al . Deep learning methods for improved decoding of linear codes [J ] . IEEE Journal of Selected Topics in Signal Processing , 2018 , 12 ( 1 ): 119 - 131 .

FUJIHASHI T , KOIKE-AKINO T , WATANABE T , et al . Nonlinear equalization with deep learning for multi-purpose visual MIMO communications [C ] // Proceedings of 2018 IEEE International Conference on Communications . Piscataway:IEEE Press , 2018 : 1 - 6 .

CAMMERER S , GRUBER T , HOYDIS J , et al . Scaling deep learning-based decoding of polar codes via partitioning [C ] // Proceedings of GLOBECOM 2017-2017 IEEE Global Communications Conference . Piscataway:IEEE Press , 2017 : 1 - 6 .

O’SHEA T , HOYDIS J . An introduction to deep learning for the physical layer [J ] . IEEE Transactions on Cognitive Communications and Networking , 2017 , 3 ( 4 ): 563 - 575 .

YE H , LI G Y . Initial results on deep learning for joint channel equalization and decoding [C ] // Proceedings of 2017 IEEE 86th Vehicular Technology Conference . Piscataway:IEEE Press , 2017 : 1 - 5 .

YE H , LI G Y , JUANG B H . Power of deep learning for channel estimation and signal detection in OFDM systems [J ] . IEEE Wireless Communications Letters , 2018 , 7 ( 1 ): 114 - 117 .

FARSAD N , GOLDSMITH A . Neural network detection of data sequences in communication systems [J ] . IEEE Transactions on Signal Processing , 2018 , 66 ( 21 ): 5663 - 5678 .

GAO X X , JIN S , WEN C K , et al . ComNet:combination of deep learning and expert knowledge in OFDM receivers [J ] . IEEE Communications Letters , 2018 , 22 ( 12 ): 2627 - 2630 .

LIN G S , MILAN A , SHEN C H , et al . RefineNet:multi-path refinement networks for high-resolution semantic segmentation [C ] // Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2017 : 5168 - 5177 .

GRAVES A , SCHMIDHUBER J . Framewise phoneme classification with bidirectional LSTM and other neural network architectures [J ] . Neural Networks , 2005 , 18 ( 5/6 ): 602 - 610 .

XU W H , ZHONG Z W , BE'ERY Y , et al . Joint neural network equalizer and decoder [C ] // Proceedings of 2018 15th International Symposium on Wireless Communication Systems (ISWCS) . Piscataway:IEEE Press , 2018 : 1 - 5 .

CACIULARU A , BURSHTEIN D . Unsupervised linear and nonlinear channel equalization and decoding using variational autoencoders [J ] . IEEE Transactions on Cognitive Communications and Networking , 2020 , 6 ( 3 ): 1003 - 1018 .

O'SHEA T J , ERPEK T , CLANCY T C . Deep learning based MIMO communications [J ] . arXiv preprint arXiv:1707.07980 , 2017 .

ALAMOUTI S M . A simple transmit diversity technique for wireless communications [J ] . IEEE Journal on Selected Areas in Communications , 1998 , 16 ( 8 ): 1451 - 1458 .

CHENG X , LIU D J , WANG C , et al . Deep learning-based channel estimation and equalization scheme for FBMC/OQAM systems [J ] . IEEE Wireless Communications Letters , 2019 , 8 ( 3 ): 881 - 884 .

汪周飞 , 袁伟娜 . 基于深度学习的多载波系统信道估计与检测 [J ] . 浙江大学学报(工学版) , 2020 , 54 ( 4 ): 732 - 738 .

WANG Z F , YUAN W N . Channel estimation and detection method for multicarrier system based on deep learning [J ] . Journal of Zhejiang University (Engineering Science) , 2020 , 54 ( 4 ): 732 - 738 .

AOUDIA F A , HOYDIS J . End-to-end learning of communications systems without a channel model [C ] // Proceedings of 2018 52nd Asilomar Conference on Signals,Systems,and Computers . Piscataway:IEEE Press , 2018 : 298 - 303 .

ZHAO Z Y , VURAN M C , GUO F J , et al . Deep-waveform:a learned OFDM receiver based on deep complex-valued convolutional networks [J ] . IEEE Journal on Selected Areas in Communications , 2021 , 39 ( 8 ): 2407 - 2420 .

YE H , LI G Y , JUANG B H F , et al . Channel agnostic end-to-end learning based communication systems with conditional GAN [C ] // Proceedings of 2018 IEEE Globecom Workshops . Piscataway:IEEE Press , 2018 : 1 - 5 .

YE H , LIANG L , LI G Y , et al . Deep learning-based end-to-end wireless communication systems with conditional GANs as unknown channels [J ] . IEEE Transactions on Wireless Communications , 2020 , 19 ( 5 ): 3133 - 3143 .

ZHANG Y , DOSHI A , LISTON R , et al . DeepWiPHY:deep learning-based receiver design and dataset for IEEE 802.11ax systems [J ] . IEEE Transactions on Wireless Communications , 2020 , 20 ( 3 ): 1596 - 1611 .

HONKALA M , KORPI D , HUTTUNEN J M J . DeepRx:fully convolutional deep learning receiver [J ] . IEEE Transactions on Wireless Communications , 2021 , 20 ( 6 ): 3925 - 3940 .

KORPI D , HONKALA M , HUTTUNEN J M J , et al . DeepRx MIMO:convolutional MIMO detection with learned multiplicative transformations [J ] . arXiv preprint arXiv:2010.16283 , 2020 .

PIHLAJASALO J , KORPI D , HONKALA M , et al . HybridDeepRx:deep learning receiver for high-EVM signals [J ] . arXiv preprint arXiv:2106.16079 , 2021 .

JIANG P , WANG T,HANB , et al . Artificial intelligence-aided OFDM receiver:design and experimental results [J ] . arXiv preprint arXiv:1812.06638 , 2018 .

YE H , LI G Y , JUANG B H . Deep learning based end-to-end wireless communication systems without pilots [J ] . IEEE Transactions on Cognitive Communications and Networking , 2021 , 7 ( 3 ): 702 - 714 .

XIE H Q , QIN Z J , LI G Y , et al . Deep learning enabled semantic communication systems [J ] . IEEE Transactions on Signal Processing , 2021 , 69 : 2663 - 2675 .

XU Z B , SUN J . Model-driven deep-learning [J ] . National Science Review , 2018 , 5 ( 1 ): 22 - 24 .

Views

800

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

A survey of deep learning based modulation recognition

GMTBLC: a deep learning-based bi-modal network traffic classification method

Research on antenna dip angle recognition method based on improved YOLOv5

Mixed signal modulation recognition method based on temporal depth residual shrinkage network

Research on fault prediction of computer network nodes driven by log information

Related Author

Shujun SUN

Shengliang PENG

Yudong YAO

Xi YANG

WEI Debin

JIANG Qinlong

WEN Jinglong

WANG Xinrui

Related Institution

College of Information Science and Engineering, Jishou University, Jishou

Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken NJ

College of Information Science and Engineering, Huaqiao University

School of Information Engineering, Dalian University

China Mobile Group Design Institute Co., Ltd.

⁰