  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
