The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Deep Learning for Spectrum Sensing
Open accessAbstract
In cognitive radio systems, the ability to accurately detect primary user's signal is essential to secondary user in order to utilize idle licensed spectrum. Conventional energy detector is a good choice for blind signal detection, while it suffers from the well-known SNR-wall due to noise uncertainty. In this letter, we firstly propose a deep learning based signal detector which exploits the underlying structural information of the modulated signals, and is shown to achieve the state of the art detection performance, requiring no prior knowledge about channel state information or background noise. In addition, the impacts of modulation scheme and sample length on performance are investigated. Finally, a deep learning based cooperative detection system is proposed, which is shown to provide substantial performance gain over conventional cooperative sensing methods.
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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
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
Metadata
- Authors
- Gao, J., Yi, X., Zhong, C., Chen, X., & Zhang, Z.
- Venue
- IEEE Wireless Communications Letters
- Published
- 2019-12
- Volume
- 8
- Issue
- 6
- Pages
- 1727-1730
- ISSN
- 2162-2337 (ISSN-L)
- 2162-2345
- OpenAlex
- W2971778960
- Cited by
- 235
- References
- 2
- FWCI
- 10.88
- Type
- journal article
- Language
- en
- Open access
- green
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- Gao, J., Yi, X., Zhong, C., Chen, X., & Zhang, Z. (2019). Deep Learning for Spectrum Sensing. IEEE Wireless Communications Letters. https://doi.org/10.1109/lwc.2019.2939314
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