Interpretable Feature Learning for RF Fingerprinting via Polar MKANs

Mikhail Krasnov; Ljupcho Milosheski; Carolina Fortuna

eess.SPAug 20, 2026

Interpretable Feature Learning for RF Fingerprinting via Polar MKANs

Authors: Mikhail Krasnov, Ljupcho Milosheski, Carolina Fortuna

Organizations: Jožef Stefan Institute, Ljubljana, Slovenia

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.

Figures & tables

Panel of Figure 2 — Fig. 2 : Polar MKAN. (a) Exclusive connections, where $F_{\phi_{1}},F_{\phi_{2}}$ receive only phase inputs and $F_{R}$ only magnitude inputs. (b) Idealised feature-space geometry away from the phase branch cut.

	Train	Evaluation
Devices	$1000$	$100$
Bursts/device	$1$	$1$
Burst length ( $N$ )	$256$	$256$
Samples/symbol ( $L$ )	$4$	$4$
SNR range (dB)	$[10,30]$	$[10,30]$

TABLE I : Composition of the synthetic dataset enabling a controlled disentanglement study.

Architecture	D	C
CNN	$6.1\pm 4.2$	$6.7\pm 3.4$
KAN	$10.0\pm 4.7$	$9.8\pm 4.0$
MKAN	$7.4\pm 3.1$	$9.5\pm 2.9$
Polar CNN	$5.0\pm 2.4$	$5.3\pm 1.8$
Polar KAN	$12.9\pm 4.8$	$13.1\pm 4.9$
Polar MKAN	$\mathbf{57.2\pm 6.0}$	$\mathbf{47.0\pm 4.0}$

TABLE II : DCI Disentanglement (D) and Completeness (C) (%, mean

\pm

95% CI over 10 runs).

Fig. 3 : Sign consistency of the phase features vs. fraction of samples whose phase wraps (95% CI, 10 runs).

Fig. 4 : Synthetic UED ROC AUC vs. held-out DCI Disentanglement (95% CI, 10 runs).

Panel of Figure 7 — Fig. 5 : Response to a positive carrier-phase rotation on WiSig ManyTx.

	Synthetic			SingleDay			ManyTx
FE	AUC	F1	AUC comp	AUC	F1	AUC comp	AUC	F1	AUC comp
CNN	62.1 $\pm$ 2.2	24.8 $\pm$ 3.6	51.1 $\pm$ 1.7	71.0 $\pm$ 2.5	36.8 $\pm$ 5.4	66.8 $\pm$ 2.4	64.7 $\pm$ 1.8	28.8 $\pm$ 3.9	62.3 $\pm$ 1.3
KAN	78.4 $\pm$ 4.2	62.9 $\pm$ 8.7	51.1 $\pm$ 1.1	89.8 $\pm$ 3.6	74.0 $\pm$ 7.0	76.2 $\pm$ 4.6	73.2 $\pm$ 2.6	44.0 $\pm$ 5.5	67.1 $\pm$ 3.1
MKAN	74.4 $\pm$ 4.6	53.3 $\pm$ 8.2	49.8 $\pm$ 1.4	79.7 $\pm$ 5.8	61.3 $\pm$ 7.9	66.5 $\pm$ 5.1	69.9 $\pm$ 1.2	38.2 $\pm$ 2.4	60.8 $\pm$ 2.3
Polar CNN	53.6 $\pm$ 1.6	20.6 $\pm$ 2.2	50.4 $\pm$ 0.9	64.8 $\pm$ 2.9	27.4 $\pm$ 5.9	62.6 $\pm$ 2.9	61.7 $\pm$ 1.1	24.1 $\pm$ 2.0	60.9 $\pm$ 1.0
Polar KAN	56.3 $\pm$ 5.3	23.2 $\pm$ 7.5	50.5 $\pm$ 1.7	82.8 $\pm$ 4.6	59.8 $\pm$ 7.1	71.0 $\pm$ 5.8	66.3 $\pm$ 1.8	30.9 $\pm$ 4.0	62.2 $\pm$ 2.4

TABLE III : UED results (%). AUC and F1 uncompensated, AUC comp with blind CFO compensation before training and evaluation. Std over 10 seeds (synthetic) or device folds (WiSig). Best uncompensated in bold.

Explore similar work

Out-of-Distribution (OOD) Detectors for Open-Set RF Fingerprinting
Jun 10, 2026Sudeepta Mondal, Ganesh SundaramoorthiOpen-Set Recognition OOD Detection
RTX Technology Research Center (RTRC).
Physics-Informed Structure Anchoring With Capture-Aware Prototype Calibration for Cross-Environment RF Fingerprinting
Jul 6, 2026Fengchong Yao, Jianbing Li, Qing Liu +4Domain Adaptation Wireless Communications
School of Information Systems Engineering, Information Engineering University, Zhengzhou 450001, China.
Calibrated RF-Fingerprinting Under Interference With Heterogeneous Transmission Protocols
Sep 17, 2026Tariq Abdul-Quddoos, Xiangfang Li, Lijun QianWireless Communications Model Calibration
CREDIT Center and Department of Electrical and Computer Engineering Prairie View A&M University, Texas A&M University System Prairie View, TX 77446, USA

Jun 10, 2026J/K move · Enter open · S save

Out-of-Distribution (OOD) Detectors for Open-Set RF Fingerprinting

Sudeepta Mondal, Ganesh Sundaramoorthi

RTX Technology Research Center (RTRC).

Open-Set Recognition OOD Detection

Open paper AskPDF arXiv

Radio-frequency (RF) fingerprinting systems must operate in open-world environments where signals from unknown transmitters and temporal drift introduce distribution shift at test time. Out-of-distribution (OOD) detection provides a natural framework for this problem, yet its application to RF fingerprinting (RFF) remains limited. A key barrier to their adoption is that most OOD detectors require auxiliary OOD data for parameter tuning, an assumption that is difficult to satisfy in RF environments where representative OOD data is impractical to collect. In this work, we introduce a promising set of OOD detection methods from the machine learning literature to open-set RFF domain. We present these methods within a unified mathematical framework based on information theory, which is a natural framework for communication systems. Our framework allows for the systematic analysis of methods and development of new methods. We further demonstrate the applicability of recent work on tuning OOD detectors without given OOD tuning data for open-set RFF. We evaluate on the POWDER RF fingerprinting dataset, showing that detectors tuned without any given OOD data achieve performance comparable to baselines with access to true OOD tuning data and greatly out-perform baseline approaches without access to true OOD tuning data, showcasing the practical viability for the RFF problem.

Abstract

Explore similar work

Out-of-Distribution (OOD) Detectors for Open-Set RF Fingerprinting

Physics-Informed Structure Anchoring With Capture-Aware Prototype Calibration for Cross-Environment RF Fingerprinting

Calibrated RF-Fingerprinting Under Interference With Heterogeneous Transmission Protocols