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12.01.2022 | Original Paper

Towards recurrent neural network with multi-path feature fusion for signal modulation recognition

verfasst von: Zihang Lei, Mengxi Jiang, Guangsong Yang, Tianmin Guan, Peng Huang, Yu Gu, Zhenghua Xu, Qiubo Ye

Erschienen in: Wireless Networks | Ausgabe 2/2022

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Abstract

Deep learning (DL) technology is an effective tool for automatic modulation recognition (AMR) in the field of cognitive radio (CR). Most of the existing DL-based approaches usually design a deep network with many layers, in which only refined features from the final layer are used for AMR. However, rough features from other layers (i.e., features captured in a shallow network with fewer layers) can also provide useful information for modulation recognition. These rough features are not carefully exploited in previous approaches. In this paper, we propose a novel multi-path features fusion network for AMR, in which both refined and rough features are learned. The proposed approach identifies 11 signals including digital modulation and analog modulation generated by the GNU radio and compared to the classic network in all SNR. The experiment results show that the effectiveness of our approach. Especially, our approach is able to achieve 99.04% accuracy in +18dB SNR which outperforms all comparison approaches.

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Titel: Towards recurrent neural network with multi-path feature fusion for signal modulation recognition
verfasst von: Zihang Lei
Mengxi Jiang
Guangsong Yang
Tianmin Guan
Peng Huang
Yu Gu
Zhenghua Xu
Qiubo Ye
Publikationsdatum: 12.01.2022
Verlag: Springer US
Erschienen in: Wireless Networks / Ausgabe 2/2022
Print ISSN: 1022-0038
Elektronische ISSN: 1572-8196
DOI: https://doi.org/10.1007/s11276-021-02877-8

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