| An Introduction to Deep Learning for the Physical Layer TJ O'Shea, J Hoydis IEEE Transactions on Cognitive Communications and Networking, 2017 | 1291 | 2017 |
| Convolutional Radio Modulation Recognition Networks TJ O'Shea, J Corgan, TC Clancy Engineering Applications of Neural Networks: 17th International Conference …, 2016 | 628 | 2016 |
| Over-the-air deep learning based radio signal classification TJ O’Shea, T Roy, TC Clancy IEEE Journal of Selected Topics in Signal Processing 12 (1), 168-179, 2018 | 455 | 2018 |
| Applications of machine learning to cognitive radio networks C Clancy, J Hecker, E Stuntebeck, T O'Shea Wireless Communications, IEEE 14 (4), 47-52, 2007 | 391 | 2007 |
| Deep Architectures for Modulation Recognition NE West, TJ O'Shea IEEE International Symposium on Dynamic Spectrum Access Networks, 2017 | 225 | 2017 |
| Radio machine learning dataset generation with gnu radio TJ O'shea, N West Proceedings of the GNU Radio Conference 1 (1), 2016 | 211 | 2016 |
| Deep learning based MIMO communications TJ O'Shea, T Erpek, TC Clancy arXiv preprint arXiv:1707.07980, 2017 | 180 | 2017 |
| Learning to communicate: channel auto-encoders, domain specific regularizers, and attention TJ O'Shea, K Karra, TC Clancy 2016 IEEE International Symposium on Signal Processing and Information …, 2016 | 166 | 2016 |
| Physical layer communications system design over-the-air using adversarial networks TJ O'Shea, T Roy, N West, BC Hilburn 2018 26th European Signal Processing Conference (EUSIPCO), 529-532, 2018 | 74 | 2018 |
| Unsupervised representation learning of structured radio communication signals TJ O'Shea, J Corgan, TC Clancy 2016 First International Workshop on Sensing, Processing and Learning for …, 2016 | 69 | 2016 |
| Recurrent neural radio anomaly detection TJ O'Shea, TC Clancy, RW McGwier arXiv preprint arXiv:1611.00301, 2016 | 58 | 2016 |
| Practical signal detection and classification in gnu radio TJ O’shea, TC Clancy, HJ Ebeid SDR forum technical conference (SDR), 2007 | 55 | 2007 |
| Radio transformer networks: Attention models for learning to synchronize in wireless systems TJ O'Shea, L Pemula, D Batra, TC Clancy 2016 50th Asilomar Conference on Signals, Systems and Computers, 662-666, 2016 | 54 | 2016 |
| Approximating the void: Learning stochastic channel models from observation with variational generative adversarial networks TJ O’Shea, T Roy, N West 2019 International Conference on Computing, Networking and Communications …, 2019 | 53 | 2019 |
| Semi-supervised radio signal identification TJ O'Shea, N West, M Vondal, TC Clancy 2017 19th International Conference on Advanced Communication Technology …, 2017 | 48 | 2017 |
| Physical layer deep learning of encodings for the MIMO fading channel TJ O'Shea, T Erpek, TC Clancy 2017 55th Annual Allerton Conference on Communication, Control, and …, 2017 | 46 | 2017 |
| Learning approximate neural estimators for wireless channel state information T O'Shea, K Karra, TC Clancy 2017 IEEE 27th international workshop on machine learning for signal …, 2017 | 44 | 2017 |
| SIMD programming in GNURadio: Maintainable and user-friendly algorithm optimization with VOLK T Rondeau, N McCarthy, T O’Shea Conference on Communications Technologies and Software Defined Radio (SDR’12 …, 2012 | 39 | 2012 |
| Architecture for an open-source cognitive radio E Stuntebeck, T O’Shea, J Hecker, T Clancy Proceedings of the SDR forum technical conference, 2006 | 39 | 2006 |
| Deep learning for wireless communications T Erpek, TJ O’Shea, YE Sagduyu, Y Shi, TC Clancy Development and Analysis of Deep Learning Architectures, 223-266, 2020 | 33 | 2020 |
Charles ClancySVP MITRE Labs & Chief Futurist, MITREVerified email at mitre.org
