Robust deep-learning-based radio fingerprinting with fine-tuning

ABSTRACTMinute hardware imperfections in the radio-frequency circuitry of a wireless device can be leveraged as a unique fingerprint. Radio fingerprinting is a way of distinguishing a device from others of the same type at the physical layer by utilizing these hardware imperfections. Recent studies proposed to utilize deep learning over raw I/Q data for the purpose of radio fingerprinting and achieve high accuracy. Unfortunately, deep-learning-based radio finger-printing is not robust over I/Q data across different days due to significant changes in wireless channels. This study proposes to leverage fine-tuning to improve the robustness of radio fingerprinting in a cross-day scenario, where training and test I/Q data are from different days. Our experimental results suggest that transfer learning is a promising approach for robust deep-learning-based radio fingerprinting in practice.