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arXiv · 2608.08332

Lightweight PID-Based Drift Mitigation for Cellular Traffic Forecasting

John Sengendo·Zineddine Bettouche·Khalid Ali·Andreas Kassler·Fabrizio Granelli
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Abstract

As mobile networks transition from Beyond 5G (B5G) towards 6G, accurate traffic forecasting is a prerequisite for improving network management. However, with increasing heterogeneity and a massive surge in connected devices, combined with dynamically evolving traffic patterns, accurate forecasting is a persistent bottleneck. Existing frameworks, while generally effective, often lack efficiency and degrade under drift, thus requiring costly model retraining to restore performance. In this paper, we propose a lightweight error correction framework that improves forecasting accuracy by integrating a Proportional-Integral-Derivative (PID) controller as a correction layer enhancing Hierarchical Spatio-temporal Models (HiSTM). Unlike retraining-based model adaptation, our framework performs online error correction without modifying the model parameters. Results from the proposed framework, evaluated across drift scenarios and cell-level analysis, demonstrate reduced Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), achieving an average drift mitigation of up to 30.18\% in MAE and 26.68\% in RMSE, thereby validating the robustness of the PID framework as a drift mitigation mechanism for network traffic forecasting.

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John Sengendo, Zineddine Bettouche, Khalid Ali, Andreas Kassler, Fabrizio Granelli. 2026-08-08. Lightweight PID-Based Drift Mitigation for Cellular Traffic Forecasting. https://arxiv.org/abs/2608.08332

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