Interpretable Feature Learning for RF Fingerprinting via Polar MKANs

2026-08-20

AI Summary

该研究提出Polar MKAN方法，通过构建可解释的特征学习模型解决RF指纹识别中深度学习模型不透明的问题。

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.

Problem

• RF fingerprinting
• I/Q impairments
• deep learning feature extractors
• security critical settings

Innovation

• Polar MKAN
• RF Fingerprinting
• Monotonic Encoder
• Channel Separation
• DCI Disentanglement