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.

翻译：射频指纹识别系统需在开放世界环境中运行，此时来自未知发射机的信号与时域漂移会在测试阶段引入分布偏移。分布外（OOD）检测为该问题提供了天然框架，但其在射频指纹识别（RFF）中的应用仍十分有限。应用OOD检测器的主要障碍在于：大多数检测器需要辅助OOD数据进行参数调优，而在射频环境中难以满足收集代表性OOD数据的假设。本研究将机器学习文献中一系列有前景的OOD检测方法引入开放集RFF领域。我们基于信息论这一通信系统的天然框架，将这些方法统一于数学框架下，该框架支持对现有方法进行系统性分析并开发新方法。我们进一步论证了近期无需OOD调优数据即可调优检测器的工作在开放集RFF中的适用性。基于POWDER射频指纹数据集进行的评估表明：在完全无OOD数据条件下调优的检测器，其性能与使用真实OOD调优数据的基线方法相当，且显著优于无法访问真实OOD调优数据的基线方法，从而证明了该方法在RFF问题中的实际可行性。