RF Distillation Diffusion Model: An Efficient RFF Data Augmentation Method

Radio Frequency Fingerprint (RFF) based physical layer authentication technology provides enhanced security for wireless communications. However, the spatiotemporal overlap of wireless signals makes it challenging to label wireless device samples. Moreover, generative networks, such as Generative Adversarial Networks (GANs) struggle to retain the subtle signal features. Utilizing existing unlabeled samples poses a significant challenge for RFF data augmentation. This paper proposes the RF Distillation Diffusion (RFDD) model, an efficient method for RFF data augmentation. RFDD employs a conditional diffusion model to generate high-quality RF signals, which fully utilizes existing unlabeled samples to learn the data distribution of signals, and labeled samples are used to enhance the capability of RFF feature extraction. Additionally, knowledge distillation is utilized to improve sampling efficiency. Experimental results show that the RFDD can fully use 20% unlabeled samples to generate high-quality synthetic RF signals within only 0.45 seconds and improve RFF identification accuracy by 25.63% with 40% synthetic signals under SNRs from -5 to 5 dB. The code is available at https://github.com/XMU-Kai/RFDD