【文献汇总】2019-2021最新应用深度学习到OFDM通信系统中的论文汇总(实时更新)

た入场券 2022-11-13 01:51 118阅读 0赞

#  应用深度学习到OFDM通信系统中的论文汇总 #

# [实时更新-Github地址][-Github] #

## 最新更新日期：2021-4-17 ##

# 2021年 #

*  [【Intelligent Radio Signal Processing: A Survey】][Intelligent Radio Signal Processing_ A Survey]

> 2021年面向深度学习的信号处理综述

*  [Model Aided Deep Learning Based MIMO OFDM Receiver With Nonlinear Power Amplifiers][]

> 发表时间：2021-5月  
> 简介：有非线性功率放大器的基于模型辅助深度学习的 MIMO OFDM 接收器。接收器由传统的最小二乘 (LS) 信道估计和迫零 (ZF) 均衡模型辅助。  
> 发表会议：2021 IEEE Wireless Communications and Networking Conference (WCNC)

*  [【Deep Joint Source Channel Coding for WirelessImage Transmission with OFDM】][Deep Joint Source Channel Coding for WirelessImage Transmission with OFDM]

> 发表时间：2021-1月  
> OFDM的无线图像传输的深层联合源信道编码

*  [【Deep Joint Source Channel Coding for WirelessImage Transmission with OFDM】][Deep Joint Source Channel Coding for WirelessImage Transmission with OFDM 1]

> 发表时间：2021年  
> 提出的模型驱动的机器学习方法消除了对单独的源代码和信道编码的需求，同时集成了OFDM数据路径以应对多路径衰落信道  
> [开源源码][Link 1]

*  [【Trimming the Fat from OFDM: Pilot- and CP-less Communication with End-to-end Learning】][Trimming the Fat from OFDM_ Pilot- and CP-less Communication with End-to-end Learning]

> 发表时间：2021-1月  
> 消除OFDM带来的麻烦：采用端到端学习的无导频和无CP通信

*  [【Reduction of PAPR by Convolutional Neural Network with Soft Feed-back in an Underwater Acoustic OFDM Communication】][Reduction of PAPR by Convolutional Neural Network with Soft Feed-back in an Underwater Acoustic OFDM Communication]

> 发表时间：2021-1月  
> 简介：水下声OFDM通信中带软反馈的卷积神经网络降低PAPR  
> 2021 International Bhurban Conference on Applied Sciences and Technologies (IBCAST)

*  [【Using Channel State Information for Physical Tamper Attack Detection in OFDM Systems: A Deep Learning Approach】][Using Channel State Information for Physical Tamper Attack Detection in OFDM Systems_ A Deep Learning Approach]

> 发表时间：2021-4月  
> 简介：使用信道状态信息进行OFDM系统中的物理篡改攻击检测：一种深度学习方法  
> IEEE Wireless Communications Letters ( Early Access )

*  【OFDM Receiver Using Deep Learning: Redundancy Issues】

> 发表时间：2021-1月  
> 简介：使用深度学习的OFDM接收器：冗余问题  
> 2020 28th European Signal Processing Conference (EUSIPCO)

*  [【Identification of OFDM-Based Radios Under Rayleigh Fading Using RF-DNA and Deep Learning】][Identification of OFDM-Based Radios Under Rayleigh Fading Using RF-DNA and Deep Learning]

> 发表时间： 2021-1月  
> 简介：使用RF-DNA和深度学习在瑞利衰落下识别基于OFDM的无线电  
> IEEE Access ( Volume: 9)

*  [【Channel Estimation based on Deep Learning in Vehicle-to-everything Environments】][Channel Estimation based on Deep Learning in Vehicle-to-everything Environments]

> 发表时间：2021－２月  
> 简介：到所有环境中基于深度学习的渠道估计  
> IEEE Communications Letters ( Early Access )

*  [【DeepRx: Fully Convolutional Deep Learning Receiver】][DeepRx_ Fully Convolutional Deep Learning Receiver]

> 发表时间：2021-2月  
> 简介：DeepRx：完全卷积深度学习接收器  
> IEEE Transactions on Wireless Communications ( Early Access )

*  [【RCNet: Incorporating Structural Information into Deep RNN for Online MIMO-OFDM Symbol Detection with Limited Training】][RCNet_ Incorporating Structural Information into Deep RNN for Online MIMO-OFDM Symbol Detection with Limited Training]

> 发表时间：2021-1月  
> 简介：RCNet：将结构信息整合到深度RNN中，以进行有限培训的在线MIMO-OFDM符号检测  
> IEEE Transactions on Wireless Communications ( Early Access )

*  [【Learning to Localize: A 3D CNN Approach to User Positioning in Massive MIMO-OFDM Systems】][Learning to Localize_ A 3D CNN Approach to User Positioning in Massive MIMO-OFDM Systems]

> 2021-2月，学习定位：大规模MIMO-OFDM系统中用户定位的3D CNN方法  
> IEEE Transactions on Wireless Communications ( Early Access )

*  [【Analysis of NOMA-OFDM 5G wireless system using deep neural network】][Analysis of NOMA-OFDM 5G wireless system using deep neural network]

> 发表时间：2021-3月  
> 简介：基于深度神经网络的NOMA-OFDM 5G无线系统分析  
> The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology

# 2020年 #

*  [【Learning for Detection: MIMO-OFDM Symbol Detection Through Downlink Pilots】][Learning for Detection_ MIMO-OFDM Symbol Detection Through Downlink Pilots]

> 2020 IEEE Transactions on Wireless Communications

*  [【Pruning the Pilots: Deep Learning-Based Pilot Design and Channel Estimation for MIMO-OFDM Systems】][Pruning the Pilots_ Deep Learning-Based Pilot Design and Channel Estimation for MIMO-OFDM Systems]

> 2020年

*  [【Learning to Demodulate from Few Pilots via Offline and Online Meta-Learning】 ][Learning to Demodulate from Few Pilots via Offline and Online Meta-Learning_]

> 2020 IEEE Transactions on Signal Processing  
> [GitHUB源码][GitHUB]

*  [【Deep Learning Based Equalizer for MIMO-OFDM Systems with Insufficient Cyclic Prefix】 ][Deep Learning Based Equalizer for MIMO-OFDM Systems with Insufficient Cyclic Prefix_]

> 2020 IEEE 92nd Vehicular Technology Conference

*  [【Deep Learning for Joint Channel Estimation and Signal Detection in OFDM Systems】][Deep Learning for Joint Channel Estimation and Signal Detection in OFDM Systems]

> 2020 IEEE Communications Letters

*  [【Model-Driven Channel Estimation for OFDM Systems Based on Image Super-Resolution Network】][Model-Driven Channel Estimation for OFDM Systems Based on Image Super-Resolution Network]

> 2020 IEEE 5th International Conference on Signal and Image Processing  
> [阅读笔记][Link 2]

*  [【Deep Learning for Joint Channel Estimation and Signal Detection in OFDM Systems】][Deep Learning for Joint Channel Estimation and Signal Detection in OFDM Systems 1]

> 2020-8月发表  
> 深度学习用于OFDM系统中的联合信道估计和信号检测

> 2020-6月，减少导频：用于MIMO-OFDM系统的基于深度学习的导频设计和信道估计

# 2019年 #

*  [【Deep Learning-Based Detector for OFDM-IM】][Deep Learning-Based Detector for OFDM-IM]

> 2019 IEEE Wireless Communications Letters

*  [【Deep Learning Based on Orthogonal Approximate Message Passing for CP-Free OFDM】 ][Deep Learning Based on Orthogonal Approximate Message Passing for CP-Free OFDM_]

> ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing

*  [【Online Extreme Learning Machine-Based Channel Estimation and Equalization for OFDM Systems】 ][Online Extreme Learning Machine-Based Channel Estimation and Equalization for OFDM Systems_]

> 2019 IEEE Communications Letters

*  [【Learning How to Demodulate from Few Pilots via Meta-Learning】][Learning to Demodulate from Few Pilots via Offline and Online Meta-Learning_]

> 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications

*  [【RoemNet: Robust Meta Learning Based Channel Estimation in OFDM Systems】][RoemNet_ Robust Meta Learning Based Channel Estimation in OFDM Systems]

> ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

*  [【A Novel OFDM Equalizer for Large Doppler Shift Channel through Deep Learning】 ][A Novel OFDM Equalizer for Large Doppler Shift Channel through Deep Learning_]

> 2019 IEEE 90th Vehicular Technology Conference

*  [【CNN and RNN-based Deep Learning Methods for Digital Signal Demodulation】][CNN and RNN-based Deep Learning Methods for Digital Signal Demodulation]

> IVSP 2019: Proceedings of the 2019 International Conference on Image, Video and Signal ProcessingFebruary 2019

*  [【“Machine LLRning”: Learning to Softly Demodulate】][Machine LLRning_ Learning to Softly Demodulate]

> 2019 IEEE Globecom Workshops (GC Wkshps)

*  [【Doppler Invariant Demodulation for Shallow Water Acoustic Communications Using Deep Belief Networks】 ][Doppler Invariant Demodulation for Shallow Water Acoustic Communications Using Deep Belief Networks_]

> 2019 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS)

[-Github]: https://github.com/823316627bandeng/Deep-learning-paper-for-OFDM-Communication
[Intelligent Radio Signal Processing_ A Survey]: https://deeplearn.org/arxiv/198162/intelligent-radio-signal-processing:-a-survey
[Model Aided Deep Learning Based MIMO OFDM Receiver With Nonlinear Power Amplifiers]: https://arxiv.org/pdf/2105.14458.pdf
[Deep Joint Source Channel Coding for WirelessImage Transmission with OFDM]: https://arxiv.org/abs/2101.03909
[Deep Joint Source Channel Coding for WirelessImage Transmission with OFDM 1]: https://www.imperial.ac.uk/media/imperial-college/research-centres-and-groups/ipc-lab/Kurka_deepJSSC_ICASSP2019.pdf
[Link 1]: https://paperswithcode.com/paper/deep-joint-source-channel-coding-for
[Trimming the Fat from OFDM_ Pilot- and CP-less Communication with End-to-end Learning]: https://arxiv.org/pdf/2101.08213v3.pdf
[Reduction of PAPR by Convolutional Neural Network with Soft Feed-back in an Underwater Acoustic OFDM Communication]: https://ieeexplore.ieee.org/document/9393026
[Using Channel State Information for Physical Tamper Attack Detection in OFDM Systems_ A Deep Learning Approach]: https://ieeexplore.ieee.org/document/9403404
[Identification of OFDM-Based Radios Under Rayleigh Fading Using RF-DNA and Deep Learning]: https://ieeexplore.ieee.org/document/9330580
[Channel Estimation based on Deep Learning in Vehicle-to-everything Environments]: https://ieeexplore.ieee.org/document/9355192
[DeepRx_ Fully Convolutional Deep Learning Receiver]: https://ieeexplore.ieee.org/document/9345504
[RCNet_ Incorporating Structural Information into Deep RNN for Online MIMO-OFDM Symbol Detection with Limited Training]: https://ieeexplore.ieee.org/document/9332284
[Learning to Localize_ A 3D CNN Approach to User Positioning in Massive MIMO-OFDM Systems]: https://ieeexplore.ieee.org/document/9364875
[Analysis of NOMA-OFDM 5G wireless system using deep neural network]: https://journals.sagepub.com/doi/abs/10.1177/1548512921999108
[Learning for Detection_ MIMO-OFDM Symbol Detection Through Downlink Pilots]: https://ieeexplore.ieee.org/document/9020011
[Pruning the Pilots_ Deep Learning-Based Pilot Design and Channel Estimation for MIMO-OFDM Systems]: https://deeplearn.org/arxiv/200643/pruning-the-pilots:-deep-learning-based-pilot-design-and-channel-estimation-for-mimo-ofdm-systems
[Learning to Demodulate from Few Pilots via Offline and Online Meta-Learning_]: https://ieeexplore.ieee.org/document/9290055
[GitHUB]: https://github.com/sangwoo-p/meta-demodulator?utm_source=catalyzex.com
[Deep Learning Based Equalizer for MIMO-OFDM Systems with Insufficient Cyclic Prefix_]: https://ieeexplore.ieee.org/document/9348509
[Deep Learning for Joint Channel Estimation and Signal Detection in OFDM Systems]: https://ieeexplore.ieee.org/document/9159626/
[Model-Driven Channel Estimation for OFDM Systems Based on Image Super-Resolution Network]: https://ieeexplore.ieee.org/abstract/document/9339375
[Link 2]: https://betterbench.blog.csdn.net/article/details/115219168
[Deep Learning for Joint Channel Estimation and Signal Detection in OFDM Systems 1]: https://arxiv.org/abs/2008.03977
[Pruning the Pilots_ Deep Learning-Based Pilot Design and Channel Estimation for MIMO-OFDM Systems 1]: https://arxiv.org/pdf/2006.11796v3.pdf
[Deep Learning-Based Detector for OFDM-IM]: https://ieeexplore.ieee.org/document/8684894
[Deep Learning Based on Orthogonal Approximate Message Passing for CP-Free OFDM_]: https://ieeexplore.ieee.org/document/8682639
[Online Extreme Learning Machine-Based Channel Estimation and Equalization for OFDM Systems_]: https://ieeexplore.ieee.org/document/8715649
[RoemNet_ Robust Meta Learning Based Channel Estimation in OFDM Systems]: https://ieeexplore.ieee.org/document/8761319
[A Novel OFDM Equalizer for Large Doppler Shift Channel through Deep Learning_]: https://ieeexplore.ieee.org/document/8891326
[CNN and RNN-based Deep Learning Methods for Digital Signal Demodulation]: https://dl.acm.org/doi/abs/10.1145/3317640.3317656
[Machine LLRning_ Learning to Softly Demodulate]: https://ieeexplore.ieee.org/abstract/document/9024433/
[Doppler Invariant Demodulation for Shallow Water Acoustic Communications Using Deep Belief Networks_]: https://ieeexplore.ieee.org/document/8851669