%0 Journal Article %1 Yuan2021multi %A Yuan, Lu %A Zhang, Hao %A Xu, Ming %A Zhou, Fuhui %A Wu, Qihui %D 2021 %I IEEE %J IEEE Internet of Things Journal %K Convolution,Convolutional Evolution,Signal Term Things,Long classification,high communication,Wireless convergence convolutional network networks,Internet neural noise of ratio,Training,Wireless speed.,multi-scale technique to %N c %P 9--10 %R 10.1109/JIOT.2021.3132652 %T A Multi-Scale CNN Framework for Wireless Technique Classification in Internet of Things %V 4662 %X Wireless technique classification (WTC) is of crucial importance in Internet of Things for realizing efficient spectrum sharing and interference management. However, the existing deep learning-based methods have low classification accuracy, especially at low SNR levels. In this paper, a multi-scale convolutional neural network framework is proposed for WTC. A multi-scale module is exploited to capture the higher abstraction features. Simulation results demonstrate that our proposed scheme can achieve a better classification performance and a higher convergence speed compared to the state-of-the-art schemes.

@article{Yuan2021multi, abstract = {Wireless technique classification (WTC) is of crucial importance in Internet of Things for realizing efficient spectrum sharing and interference management. However, the existing deep learning-based methods have low classification accuracy, especially at low SNR levels. In this paper, a multi-scale convolutional neural network framework is proposed for WTC. A multi-scale module is exploited to capture the higher abstraction features. Simulation results demonstrate that our proposed scheme can achieve a better classification performance and a higher convergence speed compared to the state-of-the-art schemes.}, added-at = {2023-02-27T14:12:01.000+0100}, author = {Yuan, Lu and Zhang, Hao and Xu, Ming and Zhou, Fuhui and Wu, Qihui}, biburl = {https://www.bibsonomy.org/bibtex/21f417d2581ca2950f77fae518ca16a59/haozhangcn}, doi = {10.1109/JIOT.2021.3132652}, file = {:F\:/OneDrive - nuaa.edu.cn/mendeley/2021-12-06_wtc.pdf:pdf}, interhash = {7130ea36971e523007130b7d7e7bb446}, intrahash = {1f417d2581ca2950f77fae518ca16a59}, issn = {23274662}, journal = {IEEE Internet of Things Journal}, keywords = {Convolution,Convolutional Evolution,Signal Term Things,Long classification,high communication,Wireless convergence convolutional network networks,Internet neural noise of ratio,Training,Wireless speed.,multi-scale technique to}, number = {c}, pages = {9--10}, publisher = {IEEE}, timestamp = {2023-02-27T14:12:15.000+0100}, title = {{A Multi-Scale CNN Framework for Wireless Technique Classification in Internet of Things}}, volume = 4662, year = 2021 }