# A Representation Learning Approach to Feature Drift Detection in Wireless Networks

arXiv: https://arxiv.org/abs/2505.10325 · HTML: https://mlsanity.com/paper/2505.10325/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 51 references that are in the map.
- First posted on arXiv 15 May 2025.

## Builds on (most cited first, 14 of 14)

- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019)
- [Toward a 6G AI-Native Air Interface](https://mlsanity.com/paper/2012.08285/) (2020)
- [LiBRe: A Practical Bayesian Approach to Adversarial Detection](https://mlsanity.com/paper/2103.14835/) (2021)
- [AI-Native Network Slicing for 6G Networks](https://mlsanity.com/paper/2105.08576/) (2021)
- [Detection of data drift and outliers affecting machine learning model performance over time](https://mlsanity.com/paper/2012.09258/) (2020)
- [Unsupervised Concept Drift Detection from Deep Learning Representations in Real-time](https://mlsanity.com/paper/2406.17813/) (2024)
- [LEAF: Navigating Concept Drift in Cellular Networks](https://mlsanity.com/paper/2109.03011/) (2021)
- [Resource-aware Time Series Imaging Classification for Wireless Link Layer Anomalies](https://mlsanity.com/paper/2104.00972/) (2021)
- [Localization as a key enabler of 6G wireless systems: A comprehensive survey and an outlook](https://mlsanity.com/paper/2302.02107/) (2023)
- [Autoregressive based Drift Detection Method](https://mlsanity.com/paper/2203.04769/) (2022)
- [Data-driven deep density estimation](https://mlsanity.com/paper/2107.11085/) (2021)
- [Open-Source Drift Detection Tools in Action: Insights from Two Use Cases](https://mlsanity.com/paper/2404.18673/) (2024)
- [An Overview and Solution for Democratizing AI Workflows at the Network Edge](https://mlsanity.com/paper/2407.11905/) (2024)

## Abstract (arXiv)

AI is foreseen to be a centerpiece in next generation wireless networks enabling enabling ubiquitous communication as well as new services. However, in real deployment, feature distribution changes may degrade the performance of AI models and lead to undesired behaviors. To counter for undetected model degradation, we propose ALERT; a method that can detect feature distribution changes and trigger model re-training that works well on two wireless network use cases: wireless fingerprinting and link anomaly detection. ALERT includes three components: representation learning, statistical testing and utility assessment. We rely on MLP for designing the representation learning component, on Kolmogorov-Smirnov and Population Stability Index tests for designing the statistical testing and a new function for utility assessment. We show the superiority of the proposed method against ten standard drift detection methods available in the literature on two wireless network use cases.