Modulation Recognition for Industrial Internet of Things Communication Signals Under Few-Shot Conditions Based on Attention Mechanism and Relation Network

Abstract

In open, interference-prone scenarios, the scarcity of precisely annotated signal samples limits the application of deep learning–based modulation identification, which generally relies on extensive labeled data for stability. Relation Networks, as an emerging class of deep learning models, exhibit rapid convergence in few-shot learning tasks. Motivated by the fast convergence property of relation-based learning and practical deployment requirements, this study develops a deep learning framework for modulation recognition under few-shot conditions. Specifically, a lightweight attention mechanism is integrated into IQCNet to boost channel and temporal feature extraction. The resulting module acts as the embedding function in a Relation Network for few-shot modulation recognition. The proposed approach offers a reliable solution for modulation recognition of communication signals operating in complex and challenging electromagnetic scenarios.