# Interpretable Feature Learning for RF Fingerprinting via Polar MKANs

arXiv: https://arxiv.org/abs/2608.19881 · HTML: https://mlsanity.com/paper/2608.19881/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 24 references that are in the map.
- First posted on arXiv 20 Aug 2026.

## Builds on (most cited first, 15 of 15)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations](https://mlsanity.com/paper/1811.12359/) (2018)
- [KAN: Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2404.19756/) (2024)
- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [Measuring Disentanglement: A Review of Metrics](https://mlsanity.com/paper/2012.09276/) (2020)
- [A Survey on Kolmogorov-Arnold Network](https://mlsanity.com/paper/2411.06078/) (2024)
- [WiSig: A Large-Scale WiFi Signal Dataset for Receiver and Channel Agnostic RF Fingerprinting](https://mlsanity.com/paper/2112.15363/) (2021)
- [Disentangled Representation Learning for RF Fingerprint Extraction under Unknown Channel Statistics](https://mlsanity.com/paper/2208.02724/) (2022)
- [Towards Robust RF Fingerprint Identification Using Spectral Regrowth and Carrier Frequency Offset](https://mlsanity.com/paper/2412.07269/) (2024)
- [Factorized Disentangled Representation Learning for Interpretable Radio Frequency Fingerprint](https://mlsanity.com/paper/2508.12660/) (2025)
- [Cross-Receiver Generalization for RF Fingerprint Identification via Feature Disentanglement and Adversarial Training](https://mlsanity.com/paper/2510.09405/) (2025)
- [Design Principles of Zero-Shot Self-Supervised Unknown Emitter Detectors](https://mlsanity.com/paper/2511.07026/) (2025)
- [Monotonic Kolmogorov-Arnold Networks: A Theoretical and Empirical Study of Monotonicity as an Inductive Bias](https://mlsanity.com/paper/2606.17886/) (2026)

## Abstract (arXiv)

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.