Recently, deep neural networks (DNNs) based emitter recognition or identification has received increasing interest. However, most of them are purely data-driven and require a large number of labeled instances. In this paper, a new Knowledge Driven Signal Transformer (KDSiT) is proposed, which introduces the knowledge graph (KG) into a signal Transformer (ST) model for accurate emitter recognition in real-world scenarios. On the one hand, KDSiT use a unified multimodal Transformer structure to explore the latent long-range dependencies in signals, and capture the subtle differences of emitters. On the other hand, KDSiT introduces domain knowledge, such as relationships and attributes between emitters, by constructing an emitter knowledge graph. By combining the powerful feature learning capability of DNNs with the rich semantic information in KG, KDSiT can extract more discriminative features of emitters from multimodal learning, to improve the identification accuracy in degraded environments. Extensive experiments are conducted, and the results prove the superiority of KDSiT over its counterparts, especially in the case of low signal-to-noise ratio (SNR), incomplete signals, and a limited number of labeled instances.

With the advancement of cognitive radio technology (CRT) in radio communication networks, deep learning (DL) has become instrumental in enhancing spectrum efficiency. However, supervised DL methods demand extensive labeled data and incur high manual costs. Consequently, practical applications of CRT increasingly necessitate techniques capable of learning robust representations from large volumes of unlabeled data. Although recent DL advancements have driven the use of self-supervised learning (SSL) in CRT through time-domain contrastive methods, these approaches fall short in extracting high-level spectral representations due to their neglect of time-frequency features. To address these limitations, a generative SSL framework is proposed for CRT applications. First, SSL pretraining is conducted in the time-frequency domain by reconstructing masked spectrograms using a Masked Autoencoder. Then, to recover the spectrogram under extreme radio conditions, mutual information maximization is employed to extract high-level spectral information obscured by noise patterns. Additionally, an attention-based channel-spectrum fusion module is designed to automatically extract and integrate features from the channel and spectral domains. The feasibility of the proposed framework is evaluated across multiple downstream tasks on four public datasets. Experimental results demonstrate that the proposed framework significantly outperforms existing methods in various downstream tasks.