The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Deep architectures for modulation recognition
Abstract
We survey the latest advances in machine learning with deep neural networks by applying them to the task of radio modulation recognition. Results show that radio modulation recognition is not limited by network depth and further work should focus on improving learned synchronization and equalization. Advances in these areas will likely come from novel architectures designed for these tasks or through novel training methods.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
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Going deeper with convolutions
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Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks
Sainath, T. N., Vinyals, O., Senior, A., & Sak, H. (2015) · 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2015.7178838 · Cited by 1,678
Convolutional Radio Modulation Recognition Networks
O’Shea, T. J., Corgan, J., & Clancy, T. C. (2016) · Communications in Computer and Information Science · doi:10.1007/978-3-319-44188-7_16 · Cited by 1,584
Self-citationDo Deep Nets Really Need to be Deep?
Ba, J. L., & Caruana, R. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1312.6184 · Cited by 1,335
Learning the speech front-end with raw waveform CLDNNs
Sainath, T. N., Weiss, R. J., Senior, A., Wilson, K. W., & Vinyals, O. (2015) · Interspeech 2015 · doi:10.21437/interspeech.2015-1 · Cited by 430
Radio Machine Learning Dataset Generation with GNU Radio
O’Shea, T. J. (2025) · TIB Data Manager · doi:10.57702/h54kawox · Cited by 303
Self-citationRadio transformer networks: Attention models for learning to synchronize in wireless systems
O’Shea, T. J., Pemula, L., Batra, D., & Clancy, T. C. (2016) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/acssc.2016.7869126 · Cited by 98
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Cited by
Deep Learning in Mobile and Wireless Networking: A Survey
Zhang, C., Patras, P., & Haddadi, H. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2904897 · Cited by 1,768
Learning to Optimize: Training Deep Neural Networks for Interference Management
Sun, H., Chen, X., Shi, Q., Hong, M., Fu, X., & Sidiropoulos, N. D. (2018) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2018.2866382 · Cited by 874
Deep Learning Models for Wireless Signal Classification With Distributed Low-Cost Spectrum Sensors
Rajendran, S., Meert, W., Giustiniano, D., Lenders, V., & Pollin, S. (2018) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2018.2835460 · Cited by 868
Deep neural network architectures for modulation classification
Liu, X., Yang, D., & Gamal, A. E. (2017) · 2017 51st Asilomar Conference on Signals, Systems, and Computers · doi:10.1109/acssc.2017.8335483 · Cited by 367
Automatic modulation classification using recurrent neural networks
Hong, D., Zhang, Z., & Xu, X. (2017) · 2017 3rd IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/compcomm.2017.8322633 · Cited by 257
Metadata
- Authors
- West, N. E., & O'Shea, T.
- Venue
- 2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)
- Publisher
- IEEE
- Published
- 2017-03
- Pages
- 1-6
- OpenAlex
- W2603396821
- Cited by
- 567
- References
- 9
- FWCI
- 16.89
- Type
- conference paper
- Language
- en
- Open access
- closed
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- West, N. E., & O'Shea, T. (2017). Deep architectures for modulation recognition. 2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN). https://doi.org/10.1109/dyspan.2017.7920754
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