Abstract
Automatic modulation recognition (AMR) is one of the essential techniques and a di cult challenge to crack in non-cooperative communication systems. Attention mechanisms have been widely applied to deep learning- based (DL) AMR, and its effectiveness has been proven. However, these methods still have problems of high complexity and low accuracy. This letter proposes a high-performance, lightweight framework combined with multi-scale grouped convolution (MGC) and second-order channel attention (SCA), named SCA-MGCLSTM. The MGC structure ensures that channel independent multi-scale depth features are extracted while considerably reducing the number of model parameters. Meanwhile, unlike general attention mechanisms that use first-order information, SCA leverages second-order information from feature maps to obtain more effective attention scores. Experiments on benchmark datasets show that our model outperforms existing deep learning methods regarding training speed and recognition accuracy.
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Acknowledgement
The work was supported in part by Sichuan Science and Technology Program (No.: 2022NSFSC0531).
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Liu, X., Zhang, J. (2024). Second-Order Channel Attention Multi-scale Grouped Convolution LSTM Networks for Automatic Modulation Recognition. In: Dong, J., Zhang, L., Cheng, D. (eds) Proceedings of the 2nd International Conference on Internet of Things, Communication and Intelligent Technology. IoTCIT 2023. Lecture Notes in Electrical Engineering, vol 1197. Springer, Singapore. https://doi.org/10.1007/978-981-97-2757-5_34
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DOI: https://doi.org/10.1007/978-981-97-2757-5_34
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