面向5G O-RAN性能指标的机器学习驱动异常检测
Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics
AI总结：
针对5G/6G网络故障管理需求，本文基于O-RAN架构提出两种机器学习异常检测算法，可主动识别UE吞吐量风险、过滤异常邻小区，减少切换故障与吞吐量下降，支撑6G自愈合网络建设。
摘要：
关键业务对网络基础设施的依赖度不断提升，加之超5G/6G网络运营复杂度日益增加，亟需主动化、自动化的网络故障管理机制。Open RAN（O-RAN，开放无线接入网）规范实现了不同无线接入网（RAN）单元间的开放接口，并将AI/ML融入网络架构，为主动式网络健康监测与异常检测带来了新可能。本文利用这些优势，开发了一套异常检测框架，可主动检测用户设备（UE）可能出现的吞吐量下降，并最大限度减少切换后故障。我们提出了两种适配实际部署的可落地异常检测算法：第一种算法通过分析资源块利用率、信号质量指标等关键性能指标（KPI），识别存在严重吞吐量下降风险的UE，从而支持主动发起切换；第二种算法评估邻小区的无线覆盖质量，过滤掉信号强度或干扰水平异常的小区，可将切换候选目标平均减少41.27%。两种方法相结合，既能缓解切换后故障与吞吐量下降问题，运行速度也远快于近实时延迟约束，为6G自愈合网络的发展奠定了基础。
英文摘要：
The ever-increasing reliance of critical services on network infrastructure coupled with the increased operational complexity of beyond-5G/6G networks necessitate the need for proactive and automated network fault management. The provision for open interfaces among different radio access network\,(RAN) elements and the integration of AI/ML into network architecture enabled by the Open RAN\,(O-RAN) specifications bring new possibilities for active network health monitoring and anomaly detection. In this paper we leverage these advantages and develop an anomaly detection framework that proactively detect the possible throughput drops for a UE and minimize the post-handover failures. We propose two actionable anomaly detection algorithms tailored for real-world deployment. The first algorithm identifies user equipment (UE) at risk of severe throughput degradation by analyzing key performance indicators (KPIs) such as resource block utilization and signal quality metrics, enabling proactive handover initiation. The second algorithm evaluates neighbor cell radio coverage quality, filtering out cells with anomalous signal strength or interference levels. This reduces candidate targets for handover by 41.27\% on average. Together, these methods mitigate post-handover failures and throughput drops while operating much faster than the near-real-time latency constraints. This paves the way for self-healing 6G networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
