A voting approach of modulation classification for wireless network-Reference-Cited by-同舟云学术

A voting approach of modulation classification for wireless network

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 6th International Conference on Networking, Systems and Security - NSysS '19
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Author:

Mahabub Atik¹,Habib Al-Zadid Sultan Bin²

Affiliation:

1. Khulna University of Engineering & Technology, Khulna, Bangladesh and Concordia University, Montreal, Quebec, Canada

2. Khulna University of Engineering & Technology, Khulna, Bangladesh and Jahangirnagar University, Savar, Dhaka, Bangladesh

Publisher

ACM Press

Reference25 articles.

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3. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770--778.

4. Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. 2017. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition. 4700--4708.

5. Jiachen Li, Lin Qi, and Yun Lin. 2016. Research on modulation identification of digital signals based on deep learning. In 2016 IEEE international conference on electronic information and communication technology (ICEICT). IEEE, 402--405.

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