Most cited papers in Wireless Signal Modulation Classification
What are the most cited papers in Wireless Signal Modulation Classification?
The most cited paper in Wireless Signal Modulation Classification is "Squeeze-and-Excitation Networks" (Jie Hu, Li Shen, Gang Zheng Sun, 2018), with 30,332 citations. Next come "An Introduction to Deep Learning for the Physical Layer" (3,031) and "Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems" (2,074).
Showing 50 of 102 papers. Every paper with more than 304 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Squeeze-and-Excitation Networks Jie Hu, Li Shen, Gang Zheng Sun | Jun 2018 | 30,332 |
| 2 | An Introduction to Deep Learning for the Physical Layer Timothy J. O’Shea, Jakob Hoydis · IEEE Transactions on Cognitive Communications and Networking | 2 Oct 2017 | 3,031 |
| 3 | Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems Hao Ye, Geoffrey Ye Li, Biing-Hwang Fred Juang · IEEE Wireless Communications Letters | 28 Sep 2017 | 2,074 |
| 4 | Guaranteeing Secrecy using Artificial Noise Satashu Goel, Rohit Negi · IEEE Transactions on Wireless Communications | Jun 2008 | 1,945 |
| 5 | Over-the-Air Deep Learning Based Radio Signal Classification Timothy J. O’Shea, Tamoghna Roy, T. Charles Clancy · IEEE Journal of Selected Topics in Signal Processing | 23 Jan 2018 | 1,629 |
| 6 | Convolutional Radio Modulation Recognition Networks Timothy J. O’Shea, Johnathan Corgan, T. Charles Clancy · Communications in computer and information science | 2016 | 1,601 |
| 7 | Deep Learning Enabled Semantic Communication Systems Huiqiang Xie, Zhijin Qin, Geoffrey Ye Li et al. · IEEE Transactions on Signal Processing | 2021 | 1,573 |
| 8 | Deep Joint Source-Channel Coding for Wireless Image Transmission Eirina Bourtsoulatze, David Burth Kurka, Denız Gündüz · IEEE Transactions on Cognitive Communications and Networking | 28 May 2019 | 1,445 |
| 9 | Survey of automatic modulation classification techniques: classical approaches and new trends Octavia A. Dobre, Ali A. Abdi, Yeheskel Bar-Ness et al. · IET Communications | Apr 2007 | 1,409 |
| 10 | Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial Mingzhe Chen, Ursula Challita, Walid Saad et al. · IEEE Communications Surveys & Tutorials | 2019 | 1,137 |
| 11 | Hierarchical digital modulation classification using cumulants Ananthram Swami, Brian M. Sadler · IEEE Transactions on Communications | Mar 2000 | 1,102 |
| 12 | Linear FMCW radar techniques A.G. Stove · IEE Proceedings F Radar and Signal Processing | 1992 | 1,050 |
| 13 | Learning to Optimize: Training Deep Neural Networks for Interference Management Haoran Sun, Xiangyi Chen, Qingjiang Shi et al. · IEEE Transactions on Signal Processing | 23 Aug 2018 | 899 |
| 14 | Deep Learning-Based Channel Estimation for Beamspace mmWave Massive MIMO Systems Hengtao He, Chao-Kai Wen, Shi Hong Jin et al. · IEEE Wireless Communications Letters | May 2018 | 865 |
| 15 | Wireless device identification with radiometric signatures Vladimir Brik, Suman Banerjee, Marco Gruteser et al. | 14 Sep 2008 | 860 |
| 16 | Deep Learning Models for Wireless Signal Classification With Distributed Low-Cost Spectrum Sensors Sreeraj Rajendran, Wannes Meert, Domenico Giustiniano et al. · IEEE Transactions on Cognitive Communications and Networking | 11 May 2018 | 859 |
| 17 | Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey Qian Mao, Fei Hu, Qi Hao · IEEE Communications Surveys & Tutorials | 2018 | 815 |
| 18 | Classifications and Applications of Physical Layer Security Techniques for Confidentiality: A Comprehensive Survey Jehad M. Hamamreh, Haji Muhammad Furqan, Hüseyin Arslan · IEEE Communications Surveys & Tutorials | 25 Oct 2018 | 763 |
| 19 | Data-Driven Deep Learning for Automatic Modulation Recognition in Cognitive Radios Yu Wang, Miao Liu, Jie Yang et al. · IEEE Transactions on Vehicular Technology | 20 Feb 2019 | 748 |
| 20 | Deep Learning in Physical Layer Communications Zhijin Qin, Hao Ye, Geoffrey Ye Li et al. · IEEE Wireless Communications | 8 Mar 2019 | 741 |
| 21 | Algorithms for automatic modulation recognition of communication signals Asoke Kumar Nandi, Elsayed Elsayed Azzouz · IEEE Transactions on Communications | Apr 1998 | 701 |
| 22 | Semantic Communications: Overview, Open Issues, and Future Research Directions Xuewen Luo, Hsiao‐Hwa Chen, Qing Guo · IEEE Wireless Communications | 13 Jan 2022 | 686 |
| 23 | Semantic Communication Systems for Speech Transmission Zhenzi Weng, Zhijin Qin · IEEE Journal on Selected Areas in Communications | 10 Jun 2021 | 670 |
| 24 | Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges Wanting Yang, Hongyang Du, Zi Qin Liew et al. · IEEE Communications Surveys & Tutorials | 18 Nov 2022 | 668 |
| 25 | Modulation Classification Based on Signal Constellation Diagrams and Deep Learning Shengliang Peng, Hanyu Jiang, Huaxia Wang et al. · IEEE Transactions on Neural Networks and Learning Systems | 24 Jul 2018 | 643 |
| 26 | Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches Fengxiao Tang, Yuichi Kawamoto, Nei Kato et al. · Proceedings of the IEEE | 6 Dec 2019 | 639 |
| 27 | Deep learning for wireless physical layer: Opportunities and challenges Tianqi Wang, Chao-Kai Wen, Hanqing Wang et al. · China Communications | Nov 2017 | 630 |
| 28 | MIMO radar waveform design based on mutual information and minimum mean-square error estimation Yang Yang, Rick S. Blum · IEEE Transactions on Aerospace and Electronic Systems | 2007 | 614 |
| 29 | Signal interception: a unifying theoretical framework for feature detection William A. Gardner · IEEE Transactions on Communications | 1988 | 611 |
| 30 | Deep architectures for modulation recognition Nathan West, Tim O’Shea | Mar 2017 | 579 |
| 31 | Deep Learning Methods for Improved Decoding of Linear Codes Eliya Nachmani, Elad Marciano, Loren Lugosch et al. · IEEE Journal of Selected Topics in Signal Processing | 2018 | 572 |
| 32 | Maximum-likelihood classification for digital amplitude-phase modulations Wen Wei, J.M. Mendel · IEEE Transactions on Communications | 2000 | 571 |
| 33 | On deep learning-based channel decoding Tobias Gruber, Sebastian Cammerer, Jakob Hoydis et al. | Mar 2017 | 556 |
| 34 | Successive refinement of information W. Equitz, Thomas M. Cover · IEEE Transactions on Information Theory | Mar 1991 | 545 |
| 35 | Deep Learning for an Effective Nonorthogonal Multiple Access Scheme Guan Gui, Hongji Huang, Yiwei Song et al. · IEEE Transactions on Vehicular Technology | 18 Jun 2018 | 534 |
| 36 | Deep Learning for RF Device Fingerprinting in Cognitive Communication Networks Kevin Merchant, Shauna Revay, George M. Stantchev et al. · IEEE Journal of Selected Topics in Signal Processing | 22 Jan 2018 | 528 |
| 37 | 6G networks. Beyond Shannon towards semantic and goal-oriented communications Emilio Calvanese Strinati, Sergio Barbarossa · HAL (Le Centre pour la Communication Scientifique Directe) | 2021 | 526 |
| 38 | On the effectiveness of secret key extraction from wireless signal strength in real environments Suman Jana, Sriram N. Premnath, Mike Clark et al. | 20 Sep 2009 | 521 |
| 39 | Learning to Detect Neev Samuel, Tzvi Diskin, Ami Wiesel · IEEE Transactions on Signal Processing | 15 Feb 2019 | 510 |
| 40 | A Lite Distributed Semantic Communication System for Internet of Things Huiqiang Xie, Zhijin Qin · IEEE Journal on Selected Areas in Communications | 9 Nov 2020 | 509 |
| 41 | Wireless Image Transmission Using Deep Source Channel Coding With Attention Modules Jialong Xu, Bo Ai, Wei Ren Chen et al. · IEEE Transactions on Circuits and Systems for Video Technology | 21 May 2021 | 502 |
| 42 | Asymptotically optimum radar detection in compound-Gaussian clutter E. Conte, Marco Lops, Giuseppe Ricci · IEEE Transactions on Aerospace and Electronic Systems | Apr 1995 | 500 |
| 43 | Model-Driven Deep Learning for Physical Layer Communications Hengtao He, Shi Hong Jin, Chao-Kai Wen et al. · IEEE Wireless Communications | 15 May 2019 | 495 |
| 44 | Device Fingerprinting in Wireless Networks: Challenges and Opportunities Qiang Xu, Rong Zheng, Walid Saad et al. · IEEE Communications Surveys & Tutorials | 3 Sep 2015 | 485 |
| 45 | Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks Jingjing Wang, Chunxiao Jiang, Haijun Zhang et al. · IEEE Communications Surveys & Tutorials | 2020 | 483 |
| 46 | On the likelihood-based approach to modulation classification Fahed Hameed, Octavia A. Dobre, Dimitrie C. Popescu · IEEE Transactions on Wireless Communications | Dec 2009 | 474 |
| 47 | Model-Driven Deep Learning for MIMO Detection Hengtao He, Chao-Kai Wen, Shi Hong Jin et al. · IEEE Transactions on Signal Processing | 2020 | 471 |
| 48 | DeepJSCC- f : Deep Joint Source-Channel Coding of Images With Feedback David Burth Kurka, Denız Gündüz · IEEE Journal on Selected Areas in Information Theory | 14 Apr 2020 | 463 |
| 49 | Task-Oriented Multi-User Semantic Communications Huiqiang Xie, Zhijin Qin, Xiaoming Tao et al. · IEEE Journal on Selected Areas in Communications | 15 Jul 2022 | 455 |
| 50 | A new approach to signal classification using spectral correlation and neural networks Albrecht J. Fehske, Joseph D. Gaeddert, Jeffrey H. Reed | 10 Dec 2005 | 452 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.