# Unsupervised Contrastive Learning for Robust RF Device Fingerprinting Under Time-Domain Shift

arXiv: https://arxiv.org/abs/2403.04036 · HTML: https://mlsanity.com/paper/2403.04036/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 30 references that are in the map.
- First posted on arXiv 6 Mar 2024.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [An Empirical Study of Training Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.02057/) (2021)
- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016)
- [A Survey of Unsupervised Deep Domain Adaptation](https://mlsanity.com/paper/1812.02849/) (2018)
- [Domain Separation Networks](https://mlsanity.com/paper/1608.06019/) (2016)
- [A Cookbook of Self-Supervised Learning](https://mlsanity.com/paper/2304.12210/) (2023)
- [Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks](https://mlsanity.com/paper/1706.00527/) (2017)
- [Analyzing and Improving Representations with the Soft Nearest Neighbor Loss](https://mlsanity.com/paper/1902.01889/) (2019)
- [Contrastive Domain Adaptation](https://mlsanity.com/paper/2103.15566/) (2021)
- [Contrastive Learning for Unsupervised Domain Adaptation of Time Series](https://mlsanity.com/paper/2206.06243/) (2022)
- [Deep Neural Network Feature Designs for RF Data-Driven Wireless Device Classification](https://mlsanity.com/paper/2105.02755/) (2021)
- [Deep-Learning-Based Device Fingerprinting for Increased LoRa-IoT Security: Sensitivity to Network Deployment Changes](https://mlsanity.com/paper/2208.14964/) (2022)
- [EPS: Distinguishable IQ Data Representation for Domain-Adaptation Learning of Device Fingerprints](https://mlsanity.com/paper/2308.04467/) (2023)
- [Domain-Adaptive Device Fingerprints for Network Access Authentication Through Multifractal Dimension Representation](https://mlsanity.com/paper/2308.07925/) (2023)
- [Uncovering the Portability Limitation of Deep Learning-Based Wireless Device Fingerprints](https://mlsanity.com/paper/2211.07687/) (2022)
- [ADL-ID: Adversarial Disentanglement Learning for Wireless Device Fingerprinting Temporal Domain Adaptation](https://mlsanity.com/paper/2301.12360/) (2023)
- [HiNoVa: A Novel Open-Set Detection Method for Automating RF Device Authentication](https://mlsanity.com/paper/2305.09594/) (2023)
- [Deep Learning-Enabled Zero-Touch Device Identification: Mitigating the Impact of Channel Variability Through MIMO Diversity](https://mlsanity.com/paper/2306.07878/) (2023)

## Built on by (most cited first, 3 of 3)

- [Unsupervised Time-Series Signal Analysis with Autoencoders and Vision Transformers: A Review of Architectures and Applications](https://mlsanity.com/paper/2504.16972/) (2025)
- [Protocol-agnostic and Data-free Backdoor Attacks on Pre-trained Models in RF Fingerprinting](https://mlsanity.com/paper/2505.00881/) (2025)
- [Generalizable and Interpretable RF Fingerprinting with Shapelet-Enhanced Large Language Models](https://mlsanity.com/paper/2602.03035/) (2026)

## Abstract (arXiv)

Radio Frequency (RF) device fingerprinting has been recognized as a potential technology for enabling automated wireless device identification and classification. However, it faces a key challenge due to the domain shift that could arise from variations in the channel conditions and environmental settings, potentially degrading the accuracy of RF-based device classification when testing and training data is collected in different domains. This paper introduces a novel solution that leverages contrastive learning to mitigate this domain shift problem. Contrastive learning, a state-of-the-art self-supervised learning approach from deep learning, learns a distance metric such that positive pairs are closer (i.e. more similar) in the learned metric space than negative pairs. When applied to RF fingerprinting, our model treats RF signals from the same transmission as positive pairs and those from different transmissions as negative pairs. Through experiments on wireless and wired RF datasets collected over several days, we demonstrate that our contrastive learning approach captures domain-invariant features, diminishing the effects of domain-specific variations. Our results show large and consistent improvements in accuracy (10.8% to 27.8%) over baseline models, thus underscoring the effectiveness of contrastive learning in improving device classification under domain shift.