Towards a Unified Few-Shot Learning Evaluation Framework for RF Fingerprinting

Radio frequency (RF) fingerprinting is a technique used to identify a wireless device based on its specific and unique hardware characteristics. In recent years, deep learning has been utilized for RF fingerprinting due to its superiority in feature extraction and higher classification accuracy. However, one major challenge of deep learning-based RF fingerprinting is that wireless signals are highly sensitive to environmental conditions, causing the device fingerprints captured in one environment to not transfer well to another. Hence, deep learning models are found to perform well in the same condition but lose their ability to classify devices in the new condition. In this paper, we examine three transfer learning techniques to mitigate the domain shift problem in RF fingerprinting and compare them with two well-defined baselines. The three RF fingerprinting datasets under various scenarios are examined to explore how environmental factors impact RF fingerprinting, such as transmitter locations, transmitter distance, and device configurations. We identify the most challenging scenarios and study how environmental factors lead to model deterioration through t-SNE visualization.