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

2026-06-10

AI Summary

This work addresses the challenge of out-of-distribution (OOD) signal detection in open-world radio frequency fingerprinting, where unknown transmitters and time-varying drifts induce distribution shifts—a problem particularly acute in realistic scenarios lacking genuine OOD tuning data. For the first time, this study systematically introduces OOD detection methods that require no real OOD tuning data into this domain. By establishing a unified information-theoretic mathematical framework, the authors propose an adaptive detection algorithm that integrates and extends multiple existing detection strategies. Experiments on the POWDER dataset demonstrate that the proposed approach achieves performance comparable to baseline methods that rely on real OOD data, while significantly outperforming current alternatives that operate without such tuning data.

Abstract

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.

Problem

• Out-of-Distribution Detection
• RF Fingerprinting
• Open-Set Recognition
• Distribution Shift
• OOD Tuning

Innovation

• Out-of-Distribution Detection
• RF Fingerprinting
• Open-Set Recognition
• Information-Theoretic Framework
• OOD Tuning-Free