Closing the Management Gap for Satellite-Integrated Community Networks: A Hierarchical Approach to Self-Maintenance

TL;DR

This paper proposes a hierarchical machine-learning approach to enable autonomous self-maintenance in satellite-integrated community networks, improving network management responsiveness and scalability.

Contribution

It introduces a novel ML-based framework for anomaly detection and mitigation tailored for satellite-integrated community networks, enhancing their autonomy.

Findings

Effective anomaly detection using recurrent neural networks

Improved network performance with ensemble mitigation methods

Demonstrated scalability in satellite and fixed backhaul scenarios

Abstract

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