The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Convolutional Radio Modulation Recognition Networks
Open accessAbstract
We study the adaptation of convolutional neural networks to the complex-valued temporal radio signal domain. We compare the efficacy of radio modulation classification using naively learned features against using expert feature based methods which are widely used today and e show significant performance improvements. We show that blind temporal learning on large and densely encoded time series using deep convolutional neural networks is viable and a strong candidate approach for this task especially at low signal to noise ratio.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893
Applications of Machine Learning to Cognitive Radio Networks
Clancy, C., Hecker, J., Stuntebeck, E., & O'Shea, T. (2007) · IEEE Wireless Communications · doi:10.1109/mwc.2007.4300983 · Cited by 328
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Cited by
An Introduction to Deep Learning for the Physical Layer
O'Shea, T., & Hoydis, J. (2017) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2017.2758370 · Cited by 2,980
Self-citationDeep 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
Fadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2707140 · Cited by 810
Modulation Classification Based on Signal Constellation Diagrams and Deep Learning
Peng, S., Jiang, H., Wang, H., Alwageed, H., Zhou, Y., Sebdani, M. M., & Yao, Y.-D. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2850703 · Cited by 632
Deep architectures for modulation recognition
West, N. E., & O'Shea, T. (2017) · 2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN) · doi:10.1109/dyspan.2017.7920754 · Cited by 567
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Metadata
- Authors
- O’Shea, T. J., Corgan, J., & Clancy, T. C.
- Venue
- Communications in Computer and Information Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 213-226
- ISSN
- 1865-0929 (ISSN-L)
- 1865-0937
- OpenAlex
- W2272847350
- Cited by
- 1,584
- References
- 3
- FWCI
- 42.84
- Type
- conference paper
- Language
- en
- Open access
- green
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- O’Shea, T. J., Corgan, J., & Clancy, T. C. (2016). Convolutional Radio Modulation Recognition Networks. Communications in Computer and Information Science. https://doi.org/10.1007/978-3-319-44188-7_16
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