Hamiltonian-Inspired Attention Mechanism for Scalable RF Transmitter Fingerprinting

ArXi:2605.30364v1 Announce Type: cross Radio-frequency (RF) fingerprinting identifies wire-less transmitters using hardware-induced imperfections present in baseband I/Q signals. However, deep learning models often degrade under receiver and channel distribution shifts, particularly as transmitter populations grow. This work proposes the Hamiltonian Transformer, a physics-informed attention architecture that enforces norm preserving value dynamics within each attention head using a learned skew-symmetric generator and a St\"ormer-Verlet leapfrog integration step.