用混合模型精准预测虚拟化基站能耗，误差低于0.5%。
ML-based Predictive Models for Power Consumption in Virtualised O-RANs
- 用DNN提取特征，再由XGBoost回归，提升预测精度。
- 混合模型均方相对误差低于0.5%，优于纯DNN模型。
- 适合网络节能管理、智能运维人员参考使用。
随着通信网络向虚拟化和解耦架构演进，能效优化成为经济与环境双重需求。传统功耗建模方法难以应对软件定义环境下复杂的非线性影响因素。本文基于硬件实测数据集，研究特征提取与回归型机器学习方法在虚拟化开放无线接入网（O-RAN）中的功耗预测性能。对比三种深度神经网络（DNN）变体：标准DNN、正则化DNN，以及结合DNN特征提取与XGBoost回归的混合模型。在不同系统参数（如传输增益、调制编码方案、空口时间）下评估模型表现。结果表明，混合模型始终领先，均方相对误差低于0.5%。该结果表明DNN-XGBoost等混合模型具备更高精度，可集成至O-RAN管理工具中，支持未来网络的更高效能调度。
原文摘要 · Abstract (English)
As communication networks adopt virtualized and disaggregated architectures, achieving energy efficiency has become increasingly important for both economic and environmental reasons. Traditional methods for power modeling are inadequate in these dynamic software-defined environments due to their inability to model complex and nonlinear factors affecting energy use. We investigate the use of feature extraction and regressor-based machine learning methods for predicting power consumption in virtualized open radio access networks (O-RANs), utilizing datasets from a hardware-instrumented testbed. We test three variants of deep neural networks (DNNs), namely, a standard DNN, a regularized DNN, and a hybrid model combining DNN-based feature extraction with an XGBoost regressor. We evaluate the performance of these models for various system parameters such as transmission gain, modulation/coding schemes, and airtime. We show that the hybrid model consistently outperformed others, achieving a mean relative error below 0.5%. Results suggest hybrid models like DNN-XGBoost offer superior accuracy and could be integrated into O-RAN management tools to enable more energy-efficient network orchestration in future networks.
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