Generalizable and Interpretable RF Fingerprinting with Shapelet-Enhanced Large Language Models

2026-02-03

AI Summary

This work addresses the limited cross-domain generalization and poor interpretability of deep neural networks in radio frequency fingerprinting by proposing a novel approach that integrates variable-length two-dimensional shapelets with a pretrained large language model. The method leverages shapelets to capture local temporal patterns from I/Q signals while employing the large language model to model long-range dependencies and global context. Notably, it enables few-shot cross-domain inference without requiring model retraining. The proposed framework not only yields interpretable feature representations but also achieves significant performance gains over existing methods across six diverse cross-protocol and cross-domain datasets, demonstrating a compelling combination of efficiency, generalizability, and interpretability.

Technology Category

• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Security and Privacy: Large-scale security measurements

Abstract

Deep neural networks (DNNs) have achieved remarkable success in radio frequency (RF) fingerprinting for wireless device authentication. However, their practical deployment faces two major limitations: domain shift, where models trained in one environment struggle to generalize to others, and the black-box nature of DNNs, which limits interpretability. To address these issues, we propose a novel framework that integrates a group of variable-length two-dimensional (2D) shapelets with a pre-trained large language model (LLM) to achieve efficient, interpretable, and generalizable RF fingerprinting. The 2D shapelets explicitly capture diverse local temporal patterns across the in-phase and quadrature (I/Q) components, providing compact and interpretable representations. Complementarily, the pre-trained LLM captures more long-range dependencies and global contextual information, enabling strong generalization with minimal training overhead. Moreover, our framework also supports prototype generation for few-shot inference, enhancing cross-domain performance without additional retraining. To evaluate the effectiveness of our proposed method, we conduct extensive experiments on six datasets across various protocols and domains. The results show that our method achieves superior standard and few-shot performance across both source and unseen domains.

Problem

• RF fingerprinting
• domain shift
• interpretability
• generalization
• black-box models

Innovation

• RF fingerprinting
• shapelets
• large language models
• domain generalization
• interpretability