论文标题

AI为6G授权Net-RCA

AI Empowered Net-RCA for 6G

论文作者

Qiu, Chengbo, Yang, Kai, Wang, Ji, Zhao, Shenjie

论文摘要

设想6G可以提供更高的数据速率,提高的可靠性,无处不在的AI服务并支持大量连接设备的规模。结果,6G将比其前任更为复杂。系统规模和复杂性的增长以及与传统网络的共存以及多元化的服务要求将不可避免地为未来的6G网络带来巨大的维护成本和努力。网络根本原因分析(NET-RCA)在识别网络故障的根本原因中起关键作用。在本文中,我们首先对设想的6G网络进行了介绍。接下来,我们讨论6G网络操作和管理的挑战和潜在解决方案,并全面调查现有的RCA方法。然后,我们提出了一个6G的人工智能(AI)授权的Net-RCA框架。进行合成和现实世界网络数据的性能比较,以证明所提出的方法的表现大大优于现有方法。

6G is envisioned to offer higher data rate, improved reliability, ubiquitous AI services, and support massive scale of connected devices. As a consequence, 6G will be much more complex than its predecessors. The growth of the system scale and complexity as well as the coexistence with the legacy networks and the diversified service requirements will inevitably incur huge maintenance cost and efforts for future 6G networks. Network Root Cause Analysis (Net-RCA) plays a critical role in identifying root causes of network faults. In this article, we first give an introduction about the envisioned 6G networks. Next, we discuss the challenges and potential solutions of 6G network operation and management, and comprehensively survey existing RCA methods. Then we propose an artificial intelligence (AI)-empowered Net-RCA framework for 6G. Performance comparisons on both synthetic and real-world network data are carried out to demonstrate that the proposed method outperforms the existing method considerably.

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