└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # 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) 2 | # 赠人玫瑰，手有余香，可以顺便Star一下啦 3 | 最新更新日期：2021-10-12 4 | # 2021 年 5 | + [【Intelligent Radio Signal Processing: A Survey】](https://deeplearn.org/arxiv/198162/intelligent-radio-signal-processing:-a-survey) 6 | >2021年面向深度学习的信号处理综述 7 | 8 | + [Model Aided Deep Learning Based MIMO OFDM Receiver With Nonlinear Power Amplifiers](https://arxiv.org/pdf/2105.14458.pdf) 9 | > 2021-5月 10 | > 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。 11 | > 2021 IEEE Wireless Communications and Networking Conference (WCNC) 12 | 13 | + [A Signal Detection Scheme Based on Deep Learning in OFDM Systems](https://arxiv.org/pdf/2107.13423.pdf) 14 | > 2021年7月 15 | > OFDM系统中基于深度学习的信号检测方案 16 | > 2021 Computer Science, Engineering, Mathematics 17 | 18 | + [【Deep Joint Source Channel Coding for WirelessImage Transmission with OFDM】](https://arxiv.org/abs/2101.03909) 19 | > 2021-1月 20 | > OFDM的无线图像传输的深层联合源信道编码 21 | + [【Deep Joint Source Channel Coding for WirelessImage Transmission with OFDM】](https://www.imperial.ac.uk/media/imperial-college/research-centres-and-groups/ipc-lab/Kurka_deepJSSC_ICASSP2019.pdf) 22 | >2021-1月 23 | >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 24 | >[开源源码](https://paperswithcode.com/paper/deep-joint-source-channel-coding-for) 25 | + [【Trimming the Fat from OFDM: Pilot- and CP-less Communication with End-to-end Learning】](https://arxiv.org/pdf/2101.08213v3.pdf) 26 | > 2021-1月 27 | > 消除OFDM带来的麻烦：采用端到端学习的无导频和无CP通信 28 | 29 | + [【Reduction of PAPR by Convolutional Neural Network with Soft Feed-back in an Underwater Acoustic OFDM Communication】](https://ieeexplore.ieee.org/document/9393026) 30 | > 2021-1月 31 | > 水下声OFDM通信中带软反馈的卷积神经网络降低PAPR 32 | > 2021 International Bhurban Conference on Applied Sciences and Technologies (IBCAST) 33 | + [【Using Channel State Information for Physical Tamper Attack Detection in OFDM Systems: A Deep Learning Approach】](https://ieeexplore.ieee.org/document/9403404) 34 | > 2021-4月 35 | > 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 36 | > IEEE Wireless Communications Letters ( Early Access ) 37 | 38 | + [【OFDM Receiver Using Deep Learning: Redundancy Issues】]() 39 | > 2021-1月 40 | > 使用深度学习的OFDM接收器：冗余问题 41 | > 2020 28th European Signal Processing Conference (EUSIPCO) 42 | 43 | + [【Identification of OFDM-Based Radios Under Rayleigh Fading Using RF-DNA and Deep Learning】](https://ieeexplore.ieee.org/document/9330580) 44 | > 2021-1月 45 | > 使用RF-DNA和深度学习在瑞利衰落下识别基于OFDM的无线电 46 | > IEEE Access ( Volume: 9) 47 | + [【Channel Estimation based on Deep Learning in Vehicle-to-everything Environments】](https://ieeexplore.ieee.org/document/9355192) 48 | > 2021－２月 49 | > 到所有环境中基于深度学习的渠道估计 50 | > IEEE Communications Letters ( Early Access ) 51 | 52 | + [【DeepRx: Fully Convolutional Deep Learning Receiver】](https://ieeexplore.ieee.org/document/9345504) 53 | > 2021-2月 54 | > DeepRx：完全卷积深度学习接收器 55 | > IEEE Transactions on Wireless Communications ( Early Access ) 56 | + [【RCNet: Incorporating Structural Information into Deep RNN for Online MIMO-OFDM Symbol Detection with Limited Training】](https://ieeexplore.ieee.org/document/9332284) 57 | > 2021-1月 58 | > 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 59 | > IEEE Transactions on Wireless Communications ( Early Access ) 60 | 61 | + [【Learning to Localize: A 3D CNN Approach to User Positioning in Massive MIMO-OFDM Systems】](https://ieeexplore.ieee.org/document/9364875) 62 | > 2021-2月 63 | > 学习定位：大规模MIMO-OFDM系统中用户定位的3D CNN方法 64 | > IEEE Transactions on Wireless Communications ( Early Access ) 65 | 66 | 67 | # 2020 年 68 | + [【Learning for Detection: MIMO-OFDM Symbol Detection Through Downlink Pilots】](https://ieeexplore.ieee.org/document/9020011) 69 | >2020 IEEE Transactions on Wireless Communications 70 | 71 | 72 | + [Pilot Pattern Design for Deep Learning-Based Channel Estimation in OFDM Systems](https://arxiv.org/pdf/2003.08980.pdf) 73 | > 2020年5月 74 | > OFDM 系统中基于深度学习的信道估计的导频模式设计 75 | 76 | [【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) 77 | >2020年 78 | + [【Learning to Demodulate from Few Pilots via Offline and Online Meta-Learning】 ](https://ieeexplore.ieee.org/document/9290055) 79 | > 2020 IEEE Transactions on Signal Processing 80 | [GitHUB源码](https://github.com/sangwoo-p/meta-demodulator?utm_source=catalyzex.com) 81 | + [【Deep Learning Based Equalizer for MIMO-OFDM Systems with Insufficient Cyclic Prefix】 ](https://ieeexplore.ieee.org/document/9348509) 82 | > 2020 IEEE 92nd Vehicular Technology Conference 83 | 84 | + [【Deep Learning for Joint Channel Estimation and Signal Detection in OFDM Systems】](https://ieeexplore.ieee.org/document/9159626/) 85 | >2020 IEEE Communications Letters 86 | 87 | + [【Model-Driven Channel Estimation for OFDM Systems Based on Image Super-Resolution Network】](https://ieeexplore.ieee.org/abstract/document/9339375) 88 | > 2020 IEEE 5th International Conference on Signal and Image Processing 89 | > [阅读笔记](https://betterbench.blog.csdn.net/article/details/115219168) 90 | 91 | + [【Deep Learning for Joint Channel Estimation and Signal Detection in OFDM Systems】](https://arxiv.org/abs/2008.03977) 92 | >2020-8月， 93 | >深度学习用于OFDM系统中的联合信道估计和信号检测 94 | + [【Pruning the Pilots: Deep Learning-Based Pilot Design and Channel Estimation for MIMO-OFDM Systems】](https://arxiv.org/pdf/2006.11796v3.pdf) 95 | > 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 96 | # 2019 年 97 | + [【Deep Learning-Based Detector for OFDM-IM】](https://ieeexplore.ieee.org/document/8684894) 98 | >2019 IEEE Wireless Communications Letters 99 | 100 | + [【Deep Learning Based on Orthogonal Approximate Message Passing for CP-Free OFDM】 ](https://ieeexplore.ieee.org/document/8682639) 101 | > ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing 102 | 103 | + [【Online Extreme Learning Machine-Based Channel Estimation and Equalization for OFDM Systems】 ](https://ieeexplore.ieee.org/document/8715649) 104 | > 2019 IEEE Communications Letters 105 | 106 | + [【Learning How to Demodulate from Few Pilots via Meta-Learning】](https://ieeexplore.ieee.org/document/9290055) 107 | >2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications 108 | 109 | + [【RoemNet: Robust Meta Learning Based Channel Estimation in OFDM Systems】](https://ieeexplore.ieee.org/document/8761319) 110 | > ICC 2019 - 2019 IEEE International Conference on Communications (ICC) 111 | 112 | + [【A Novel OFDM Equalizer for Large Doppler Shift Channel through Deep Learning】 ](https://ieeexplore.ieee.org/document/8891326) 113 | > 2019 IEEE 90th Vehicular Technology Conference 114 | 115 | + [【CNN and RNN-based Deep Learning Methods for Digital Signal Demodulation】](https://dl.acm.org/doi/abs/10.1145/3317640.3317656) 116 | > IVSP 2019: Proceedings of the 2019 International Conference on Image, Video and Signal ProcessingFebruary 2019 117 | 118 | + [【"Machine LLRning": Learning to Softly Demodulate】](https://ieeexplore.ieee.org/abstract/document/9024433/) 119 | > 2019 IEEE Globecom Workshops (GC Wkshps) 120 | 121 | + [【Doppler Invariant Demodulation for Shallow Water Acoustic Communications Using Deep Belief Networks】 ](https://ieeexplore.ieee.org/document/8851669) 122 | >2019 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS) 123 | 124 | --------------------------------------------------------------------------------