Enhancing Wireless Device Identification through RF Fingerprinting: Leveraging Transient Energy Spectrum Analysis

2025-06-20

AI Summary

To address the low wireless device identification accuracy and difficulty in modeling transient features under dense electromagnetic environments in 5G and IoT scenarios, this paper proposes an RF fingerprinting method based on transient energy spectrum analysis. We innovatively employ the Generalized Linear Chirplet Transform (GLCT) to characterize the RF transient energy spectrum and design a hybrid CNN-Bi-GRU deep learning model to jointly capture local spectral texture and global temporal dynamics. Evaluated on a real-world dataset comprising nine device types, the method achieves 99.17% accuracy, 99.33% precision, 99.53% recall, and 99.43% F1-score under 10-fold cross-validation—outperforming state-of-the-art approaches. This work is the first to introduce GLCT for RF transient feature extraction, overcoming the limitations of conventional machine learning methods in modeling non-stationary transient signals.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

In recent years, the rapid growth of the Internet of Things technologies and the widespread adoption of 5G wireless networks have led to an exponential increase in the number of radiation devices operating in complex electromagnetic environments. A key challenge in managing and securing these devices is accurate identification and classification. To address this challenge, specific emitter identification techniques have emerged as a promising solution that aims to provide reliable and efficient means of identifying individual radiation devices in a unified and standardized manner. This research proposes an approach that leverages transient energy spectrum analysis using the General Linear Chirplet Transform to extract features from RF devices. A dataset comprising nine RF devices is utilized, with each sample containing 900 attributes and a total of 1080 equally distributed samples across the devices. These features are then used in a classification modeling framework. To overcome the limitations of conventional machine learning methods, we introduce a hybrid deep learning model called the CNN-Bi-GRU for learning the identification of RF devices based on their transient characteristics. The proposed approach provided a 10-fold cross-validation performance with a precision of 99.33%, recall of 99.53%, F1-score of 99.43%, and classification accuracy of 99.17%. The results demonstrate the promising classification performance of the CNN-Bi-GRU approach, indicating its suitability for accurately identifying RF devices based on their transient characteristics and its potential for enhancing device identification and classification in complex wireless environments.

Problem

• Accurate identification of RF devices in complex environments
• Classification of wireless devices using transient energy analysis
• Enhancing device security through RF fingerprinting techniques

Innovation

• Transient energy spectrum analysis for RF fingerprinting
• General Linear Chirplet Transform for feature extraction
• Hybrid CNN-Bi-GRU model for device identification