发表机构
African Institute for Mathematical Sciences (AIMS); Vodacom Group Limited; Stellenbosch University; University of the Witwatersrand（非洲数学科学研究所; 沃达康集团; 斯泰伦博斯大学; 威特沃特斯兰德大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种基于能量感知最小失真嵌入的无监督同行相对表示学习框架，用于识别移动网络站点的能效低效，其性能优于传统异常检测基线，可为大规模能效优化提供支撑。
AI 中文摘要
能耗是移动网络运营商最大的运营支出项目之一，但站点级的能效低效问题，如故障的冷却控制器、空闲的无线电设备及寄生辅助负载，往往无法被检测到，原因在于不存在能效低效的真实标签，且历史测量数据中可能已包含嵌入的低效情况。本研究提出一种无监督的同行相对方法，其前提是具有相似结构和运行特征的站点应表现出相当的能耗。为捕捉这些关系，引入了一种新颖的感知能量的最小失真嵌入（Minimum Distortion Embedding, MDE）公式，该公式通过基于能量的排斥机制扩展了标准的MDE目标。这鼓励与可比同行相比能耗异常高的站点在嵌入空间中从其局部邻域中被位移。所得的低维表示同时保留了结构相似性并编码了与能量相关的偏差，使得能够通过同行相对比较识别潜在的低效站点。推导的异常分数为现场调查的优先级排序提供了实用机制，允许移动网络运营商将工程资源集中在最可能实现节能的站点上。实验结果表明，所提出的方法优于传统的异常检测基线，并为移动网络中的大规模能效优化提供了坚实的基础。
英文摘要
Energy consumption is one of the largest operational expenditure items for mobile network operators, yet site-level energy inefficiencies such as faulty cooling controllers, idle radio equipment, and parasitic auxiliary loads often remain undetected because no ground-truth inefficiency labels exist and historical measurements may already contain embedded inefficiencies. This study proposes an unsupervised peer-relative approach based on the premise that sites with similar structural and operational characteristics should exhibit comparable energy consumption. To capture these relationships, a novel energy-aware Minimum Distortion Embedding (MDE) formulation is introduced that extends the standard MDE objective with an energy-based repulsion mechanism. This encourages sites with anomalously high energy consumption relative to comparable peers to become displaced from their local neighbourhoods in the embedding space. The resulting low-dimensional representation simultaneously preserves structural similarity and encodes energy-related deviations, enabling the identification of potentially inefficient sites through peer-relative comparison. The derived anomaly scores provide a practical mechanism for prioritising field investigations, allowing mobile network operators to focus engineering resources on sites most likely to yield energy savings. Experimental results demonstrate that the proposed approach outperforms conventional anomaly detection baselines and provides a robust foundation for large-scale energy-efficiency optimisation in mobile networks.
Comments22 pages, 6 figures
