The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Automatic modulation classification using recurrent neural networks
Abstract
Automatic modulation classification (AMC) is one of the essential technologies, and also a hard nut to crack in the field of cognitive radio (CR) and non-cooperative communication systems. In this work, we propose a novel AMC method based on the promising recurrent neural network (RNN), which is shown to have the capability to sufficiently exploit the temporal sequence characteristic of received communication signals. This method resorts to raw signals directly with limited data length, and avoids extracting signal features manually. The proposed method is compared with a convolutional neural network (CNN) based method and the result indicates the superiority of the proposed one, especially when signal-to-noise ratio (SNR) is above -4dB. Furthermore, a comparative study is presented to evaluate the availability of the other different RNN structures. And a more efficient structure is recommended based on two-layer gated recurrent unit (GRU) network. Additional numerical results demonstrate that the proposed structure achieves an improved performance from 80% to 91% in terms of classification accuracy.
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Cites
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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
Supervised Sequence Labelling with Recurrent Neural Networks
Graves, A. (2012) · Studies in Computational Intelligence · doi:10.1007/978-3-642-24797-2 · Cited by 3,120
Long short-term memory recurrent neural network architectures for large scale acoustic modeling
Sak, H., Senior, A., & Beaufays, F. (2014) · Interspeech 2014 · doi:10.21437/interspeech.2014-80 · Cited by 2,973
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
Survey of automatic modulation classification techniques: classical approaches and new trends
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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 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
Radio Machine Learning Dataset Generation with GNU Radio
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Cited by
AvgNet: Adaptive Visibility Graph Neural Network and Its Application in Modulation Classification
Xuan, Q., Zhou, J., Qiu, K., Chen, Z., Xu, D., Zheng, S., & Yang, X. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3146836 · Cited by 106
“The first is to use the RNN model to classify the signals, specifically, we use GRU and LSTM mentioned in Sec.”
Influential
Metadata
- Authors
- Hong, D., Zhang, Z., & Xu, X.
- Venue
- 2017 3rd IEEE International Conference on Computer and Communications (ICCC)
- Publisher
- IEEE
- Published
- 2017-12
- Pages
- 695-700
- OpenAlex
- W2795250928
- Cited by
- 257
- References
- 13
- FWCI
- 3.56
- Type
- conference paper
- Language
- en
- Open access
- closed
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