AI 中文总结
研究虚拟无线接入网功耗预测问题，采用基于特征提取和回归器的机器学习方法，测试深度神经网络三种变体，结果显示结合DNN与XGBoost的混合模型性能最佳，平均相对误差低于0.5%，可用于未来网络节能编排。
AI 中文摘要
随着通信网络采用虚拟化和分布式架构，出于经济和环境原因，实现能源效率变得越发重要。传统功耗建模方法在这些动态软件定义环境中存在不足，无法对影响能源使用的复杂非线性因素建模。我们利用硬件测试平台的数据集，研究基于特征提取和回归器的机器学习方法来预测虚拟开放无线接入网（O-RAN）的功耗。测试了深度神经网络（DNN）的三种变体，即标准DNN、正则化DNN以及结合基于DNN的特征提取与XGBoost回归器的混合模型。评估了这些模型在各种系统参数下的性能，如传输增益、调制/编码方案和空中时间。结果表明混合模型始终优于其他模型，平均相对误差低于0.5%。结果表明，像DNN-XGBoost这样的混合模型具有更高的准确性，可集成到O-RAN管理工具中，以在未来网络中实现更节能的网络编排。
英文摘要
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.
