AI 中文总结
针对蜂窝流量预测中模型漂移需高成本重训练的问题，提出整合PID控制器的轻量级在线误差修正框架，在漂移场景下可降低MAE达30.18%、RMSE达26.68%，提升预测效率与鲁棒性。
AI 中文摘要
随着移动网络从后5G（B5G）向6G过渡，准确的流量预测是优化网络管理的前提。然而，随着网络异构性提升、连接设备数量激增，加上流量模式动态演变，准确预测长期存在瓶颈。现有框架虽普遍有效，但往往效率不足且在漂移场景下性能下降，需耗费高昂成本重新训练模型以恢复性能。本文提出一种轻量级误差修正框架，通过将比例-积分-微分（PID）控制器作为修正层整合到分层时空模型（HiSTM）中，提升预测准确性。与基于重训练的模型适配不同，该框架无需修改模型参数即可执行在线误差修正。在漂移场景和小区级分析中评估的结果显示，该框架降低了平均绝对误差（MAE）和均方根误差（RMSE），在MAE上实现了最高30.18%的平均漂移缓解，在RMSE上实现了最高26.68%的平均漂移缓解，验证了PID框架作为网络流量预测漂移缓解机制的鲁棒性。
英文摘要
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.
CommentsAccepted at 17th International Conference on Network of the Future NoF 2026
