缩小卫星集成社区网络的管理差距：自维护的分层方法
Closing the Management Gap for Satellite-Integrated Community Networks: A Hierarchical Approach to Self-Maintenance
摘要：
社区网络已成为在世界各地无服务和服务不足地区提供基本互联网连接的重要范例。然而，CN不可或缺的一部分是网络管理，其中非常需要响应和自主维护。随着电信网络技术的进步，传统的依赖卫星的CN被设想为卫星集成CN（SICN），其将在网络管理方面具有显著的自主性、智能性和可扩展性。本文讨论了基于机器学习（ML）的分层方法，使自主自我维护的SICNs。该方法分为异常识别和异常缓解阶段，其中提出了相关的ML方法，数据汇集手段，部署选项和缓解方案。通过案例研究，我们讨论了一个典型的情况下，使用卫星和固定连接作为回程选项，并显示了所提出的方法的有效性和性能改善与递归神经网络和集成方法
英文摘要：
Community networks (CNs) have become an important paradigm for providing essential Internet connectivity in unserved and underserved areas across the world. However, an indispensable part for CNs is network management, where responsive and autonomous maintenance is much needed. With the technological advancement in telecommunications networks, a classical satellite-dependent CN is envisioned to be transformed into a satellite-integrated CN (SICN), which will embrace significant autonomy, intelligence, and scalability in network management. This article discusses the machine-learning (ML) based hierarchical approach to enabling autonomous self-maintenance for SICNs. The approach is split into the anomaly identification and anomaly mitigation phases, where the related ML methods, data collection means, deployment options, and mitigation schemes are presented. With the case study, we discuss a typical scenario using satellite and fixed connections as backhaul options and show the effectiveness and performance improvements of the proposed approach with recurrent neural network and ensemble methods
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。
