<ul><li>AI is expected to play a vital role in future wireless networks, but changes in feature distribution can impact AI models' performance negatively.</li><li>A new method called ALERT has been introduced to detect feature distribution changes, prompting model re-training in scenarios like wireless fingerprinting and link anomaly detection.</li><li>ALERT comprises representation learning, statistical testing, and utility assessment components, utilizing MLP for representation learning and Kolmogorov-Smirnov tests for statistical testing.</li><li>The proposed ALERT method has shown superior performance compared to ten standard drift detection methods in two wireless network use cases.</li></ul>

A Representation Learning Approach to Feature Drift Detection in Wireless Networks

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