Zero-Shot Modulation Recognition via Knowledge-Informed Waveform Description

In non-cooperative environments, deep learning-based automatic modulation recognition techniques often struggle with the situations with insufficient or even no training data accessible. In this letter, we investigate this problem in the amplitude-phase-modulation recognition task and introduce a knowledge-informed waveform description for zero-shot recognition generalization. Specifically, drawing inspiration from constellation association knowledge, we define a constellation-based semantic attribute set to describe waveform structures and employ graph formulation to model attributes' symmetric dependency for improving representations. Subsequently, we align the waveform and semantic spaces by associating waveform and attribute compositional representations, facilitating the transfer of knowledge from the seen to unseen domain. Our scheme can reason the labels of unseen waveform types with the guidance of the attribute description outputting, beyond merely distinguishing test instances as unseen. Experiments validate the efficacy of the proposed method across few-shot and zero-shot recognition tasks.