Toward Channel-Robust RF Fingerprint Identification Using Spectrum Averaging and High-Order Difference

This paper presents a new system for identifying Wi-Fi devices based on their radio frequency fingerprints (unique hardware characteristics that devices emit when transmitting wireless signals), using techniques that remove channel effects (distortions caused by the physical environment and distance between devices). The system uses spectrum averaging (combining signal measurements from multiple antennas to reduce environmental interference) and high-order difference algorithms (mathematical operations that isolate hardware-specific features) to achieve 90.28% accuracy in identifying devices, outperforming previous methods.