Few Sample Modulation Identification under High Dynamic Environment

Traditional modulation recognition techniques and channel coding identification methods are showing increasing limitations in terms of recognition accuracy, computational complexity, and recognition timeliness, especially in complex electromagnetic spectrum environments and strong adversarial electromagnetic environments in combat scenarios. They are no longer able to cope with complex electromagnetic spectrum environments. In recent years, with the development of artificial intelligence and machine learning, AI technologies represented by deep learning have achieved great success in computer vision, natural language processing and other fields. Applying AI to modulation recognition has also been increasingly reported. However, current AI-based modulation recognition techniques tend to use large amounts of labeled samples for supervised training, but still cannot achieve good performance under low signal-to-noise ratios and lack of prior information, making them difficult to apply in practice. Therefore, to address the challenges faced by AI-based modulation recognition in practical applications in highly dynamic environments, there is an urgent need to conduct in-depth research on few-shot modulation recognition techniques.

Related Publications

1. Hao Zhang, Fuhui Zhou, Qihui Wu, and Naofal Al-Dhahir. “SSwsrNet: A Semi-Supervised Few-Shot Learning Framework for Wireless Signal Recognition.” IEEE Transactions on Communications, vol. 72, no. 9, pp. 5823-5836, doi: 10.1109/TCOMM.2024.3385921. [HTML] [Arxiv]
2. Hao Zhang, Fuhui Zhou, Qihui Wu, and Chau Yuen. “FSOS-AMC: Few-Shot Open-Set Learning for Automatic Modulation Classification.” In 16th International Conference on Wireless Communications and Signal Processing (WCSP 2024).
3. Hao Zhang, Fuhui Zhou, Qihui Wu, and Chau Yuen.“FSOS-AMC: Few-Shot Open-Set Learning for Automatic Modulation Classification under Multipath Fading Channels”, IEEE Transactions on Wireless Communications (Under Review)