| Title | Few-Shot Learning of Signal Modulation Recognition Based on Attention Relation Network |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Zhang, Zilin, Li, Yan, Gao, Meiguo |
| Conference Name | 2020 28th European Signal Processing Conference (EUSIPCO) |
| Keywords | Attention, feature extraction, Few-shot learning, modulation, Network reconnaissance, pubcrawl, Reconnaissance, resilience, Resiliency, Robustness, Scalability, Signal Modulation Recognition, Signal to noise ratio, Training, Transforms |
| Abstract | Most of existing signal modulation recognition methods attempt to establish a machine learning mechanism by training with a large number of annotated samples, which is hardly applied to the real-world electronic reconnaissance scenario where only a few samples can be intercepted in advance. Few-Shot Learning (FSL) aims to learn from training classes with a lot of samples and transform the knowledge to support classes with only a few samples, thus realizing model generalization. In this paper, a novel FSL framework called Attention Relation Network (ARN) is proposed, which introduces channel and spatial attention respectively to learn a more effective feature representation of support samples. The experimental results show that the proposed method can achieve excellent performance for fine-grained signal modulation recognition even with only one support sample and is robust to low signal-to-noise-ratio conditions. |
| DOI | 10.23919/Eusipco47968.2020.9287608 |
| Citation Key | zhang_few-shot_2021 |
Few-Shot Learning of Signal Modulation Recognition Based on Attention Relation Network