aiqbench analysis
This research introduces Polar MKANs, a novel architecture using Kolmogorov-Arnold Networks to enhance the interpretability of RF fingerprinting systems by explicitly separating magnitude and phase features. By enforcing monotonicity in latent dimensions, the model achieves significantly higher disentanglement scores compared to standard baselines, addressing the opacity issue that hinders deep learning adoption in security-critical wireless authentication.
Interpretable Feature Learning for RF Fingerprinting via Polar MKANs
arXiv:2608.19881v1 Announce Type: cross Abstract: Radio frequency (RF) fingerprinting authenticates wireless devices from hardware-induced I/Q impairments, typically with deep learning feature extractors that are accurate but opaque, limiting their use in security critical settings. We propose Polar Monotonic Kolmogorov-Arnold Networks (Polar MKAN), a block partitioned monotonic encoder on polar inputs in which each latent dimension depends exclusively on magnitude or phase, yielding channel separation and monotone responses by construction. On a synthetic gain and carrier frequency offset (CFO) benchmark, Polar MKAN reaches 57.2 percent DCI Disentanglement versus at most 12.9 percent for unpartitioned baselines. We further evaluate the detection accuracy trade off on real data and the sensitivity to blind CFO compensation.
Abstract:Radio frequency (RF) fingerprinting authenticates wireless devices from hardware-induced I/Q impairments, typically with deep learning feature extractors that are accurate but opaque, limiting their use in security critical settings. We propose Polar Monotonic Kolmogorov-Arnold Networks (Polar MKAN), a block partitioned monotonic encoder on polar inputs in which each latent dimension depends exclusively on magnitude or phase, yielding channel separation and monotone responses by construction. On a synthetic gain and carrier frequency offset (CFO) benchmark, Polar MKAN reaches 57.2 percent DCI Disentanglement versus at most 12.9 percent for unpartitioned baselines. We further evaluate the detection accuracy trade off on real data and the sensitivity to blind CFO compensation.
Signal Processing (eess.SP); Machine Learning (cs.LG)
arXiv:2608.19881 [eess.SP]
(or arXiv:2608.19881v1 [eess.SP] for this version)
https://doi.org/10.48550/arXiv.2608.19881 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 204 words
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