Zero-Shot Automatic Modulation Recognition Using a Large Vision-Language Model
Yurui Zhao, Xiang Wang, Shuya Cao, Zhitao Huang · IEEE Transactions on Communications · 2025
Cognitive radio systems have a recognized need for Zero-Shot Automatic Modulation Recognition (ZSAMR). Previous research aimed at identifying unobserved modulation types struggles to incorporate text-format expert insights into zero-shot learning approaches for unseen modulation schemes. To overcome these limitations, we propose a novel ZSAMR framework utilizing a vision-language model (ZSAMR-VLM). The ZSAMR-VLM leverages the pre-trained vision-language model to describe multiple views of signals with preset prompts. Modulation scheme prediction is achieved by matching multi-view signal characteristics with manually defined semantic prototypes. By facilitating linguistic interactions, the ZSAMR-VLM allows signal processing experts to embed domain-specific knowledge directly into the modulation recognition process, effectively bridging the gap between modulation recognition and natural language. Our simulation experiments demonstrate that the ZSAMR-VLM can effectively implement zero-shot recognition without relying on instances from seen classes. Furthermore, comparative experiments suggest that incorporating multiple views of communication signals significantly enhances the performance of the ZSAMR-VLM. The source code is available at https://github.com/zhaoyurui/ZSAMR-VLM.