arXiv:2605.30364v1 Announce Type: cross Abstract: 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. An additional phase
The paper addresses a critical challenge in RF fingerprinting—scalability and robustness against channel shifts—which is becoming more urgent with the proliferation of wireless devices and advanced jamming techniques.
This development could significantly enhance the accuracy and resilience of identifying wireless transmitters, crucial for national security, spectrum management, and combating signal spoofing.
The proposed Hamiltonian Transformer introduces a physics-informed approach to attention mechanisms, potentially overcoming current limitations of deep learning models in maintaining performance across varying conditions.
- · Defense contractors
- · Intelligence agencies
- · National security apparatus
- · Spectrum regulators
- · Adversaries relying on signal anonymity
- · Organizations using off-the-shelf wireless communication without enhanced securi
- · Developers of less robust RF fingerprinting technologies
Improved ability to identify and track unauthorized or malicious wireless emitters.
Increased difficulty for hostile actors to hide their communication and electronic warfare activities.
Enhanced global electronic surveillance and counter-intelligence capabilities, potentially leading to new arms races in signal obfuscation and detection.
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Read at arXiv cs.AI