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

arXiv: https://arxiv.org/abs/2506.17439 · HTML: https://mlsanity.com/paper/2506.17439/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 64 references that are in the map.
- First posted on arXiv 20 Jun 2025.

## Builds on (most cited first, 11 of 11)

- [A Survey of the Recent Architectures of Deep Convolutional Neural Networks](https://mlsanity.com/paper/1901.06032/) (2019)
- [MetaLoc: Learning to Learn Wireless Localization](https://mlsanity.com/paper/2211.04258/) (2022)
- [Deep Ensembling for Perceptual Image Quality Assessment](https://mlsanity.com/paper/2305.09141/) (2023)
- [RF-PUF: Enhancing IoT Security through Authentication of Wireless Nodes using In-situ Machine Learning](https://mlsanity.com/paper/1805.01374/) (2018)
- [Transmitter Classification With Supervised Deep Learning](https://mlsanity.com/paper/1905.07923/) (2019)
- [Leaf Image-based Plant Disease Identification using Color and Texture Features](https://mlsanity.com/paper/2102.04515/) (2021)
- [PIQI: Perceptual Image Quality Index based on Ensemble of Gaussian Process Regression](https://mlsanity.com/paper/2305.09214/) (2023)
- [Radio Frequency Fingerprint Identification Based on Denoising Autoencoders](https://mlsanity.com/paper/1907.08809/) (2019)
- [Image Quality Assessment for Foliar Disease Identification (AgroPath)](https://mlsanity.com/paper/2209.12443/) (2022)
- [PSACCF: Prioritized Online Slice Admission Control Considering Fairness in 5G/B5G Networks](https://mlsanity.com/paper/2202.05447/) (2022)
- [Device Authentication Codes based on RF Fingerprinting using Deep Learning](https://mlsanity.com/paper/2004.08742/) (2020)

## Abstract (arXiv)

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.