Automatic modulation recognition algorithm based on MSCNN-BiGRU network

Xiaowei Wu; Yan Zhou

doi:10.1117/12.2646297

23 August 2022 Automatic modulation recognition algorithm based on MSCNN-BiGRU network

Xiaowei Wu, Yan Zhou

Proceedings Volume 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022); 123301K (2022) https://doi.org/10.1117/12.2646297
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 2022, Huzhou, China

Abstract

In this paper, we propose an automatic modulation recognition (AMR) algorithm based on multi-scale convolutional neural network cascaded bidirectional gated recurrent unit (MSCN-BiGRU) network. Firstly, the network extracts and fuse the multi-scale features of the raw signal I/Q data. and then obtain the global features of the fused features at a larger scale through the BiGRU network. Finally, the softmax classifier is used to complete the recognition. The experimental results show that the average recognition accuracy of the algorithm on the RML2018.01A dataset is 62.42%, surpassing CNN by 3.89% and ResNet by 2.8%, and the maximum recognition accuracy can reach 97.5%.

Citation Download Citation

Xiaowei Wu and Yan Zhou "Automatic modulation recognition algorithm based on MSCNN-BiGRU network", Proc. SPIE 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 123301K (23 August 2022); https://doi.org/10.1117/12.2646297

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