Modulation Recognition Method for Underwater Acoustic Communication Signal Based on Relation Network under Small Sample Set
Haiwang Wang, Bin Wang, Ran Wang, Qiyue Ouyang · 2021 IEEE 21st International Conference on Communication Technology (ICCT) · 2021
Aiming at the poor performance of the modulation recognition method based on deep learning when there are only a few labeled samples in the target sea area, this paper proposes a modulation recognition method for underwater acoustic communication signal with small sample set based on relation network(RN). In this paper, the signal power spectrum is selected as the shallow feature representation of the signal based on the prior information of the signal and the cognition of the task-driven training mode of the relation network. And hence a modulation recognition model based on power spectrum and relation network is designed. This model is optimized by building small sample training tasks in different channels. This training mode improves the ability of the recognition method to quickly classify when only few labeled sample is available in the target sea area. Simulation experiments and the actual signal verify the advanced performance of our algorithm. Compared with existing recognition algorithms, our method requires fewer labeled samples and has better recognition effect.