Radio Frequency Fingerprinting

Radio frequency fingerprinting (RFF) identifies wireless devices based on unique imperfections in their radio signals, aiming to enhance security and authentication. Current research heavily utilizes deep learning, employing various architectures like convolutional neural networks and attention mechanisms, to extract and classify these device-specific fingerprints, often addressing challenges like channel variations and open-set identification through techniques such as contrastive learning, prototype learning, and domain adaptation. The development of robust and scalable RFF systems holds significant potential for improving the security of IoT devices and other wireless networks by providing a physical-layer authentication method.