Lightweight PID-Based Drift Mitigation for Cellular Traffic Forecasting

2026-08-08

AI Summary

This work addresses the challenge of traffic prediction in 6G mobile networks, where existing models suffer significant performance degradation under data distribution shifts and rely on costly retraining. To overcome this, the authors propose a lightweight online error correction framework that, for the first time, integrates a proportional–integral–derivative (PID) controller into network traffic forecasting as a correction layer. This layer dynamically compensates for prediction biases from a hierarchical spatio-temporal model (HiSTM) without updating its parameters, enabling real-time adaptation to distribution drift. The resulting end-to-end online correction architecture achieves substantial improvements in accuracy and robustness, reducing mean absolute error (MAE) by 30.18% and root mean square error (RMSE) by 26.68% on average across diverse drift scenarios, while maintaining low computational overhead and high efficiency.

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.

Problem

• traffic forecasting
• concept drift
• cellular networks
• model degradation
• 6G

Innovation

• PID controller
• drift mitigation
• lightweight framework
• traffic forecasting
• online error correction