AI-based Self-healing Solutions Applied to Cellular Networks: An Overview

TL;DR

This paper reviews machine learning methods for self-healing in cellular networks, highlighting how AI can autonomously detect and repair cell outages to reduce operational costs in 4G, 5G, and future 6G networks.

Contribution

It provides a comprehensive overview of ML-based self-healing techniques, including taxonomy and state-of-the-art approaches for cellular network outage management.

Findings

ML methods effectively detect cell outages

Self-healing reduces operational costs

Deep learning enhances outage prediction accuracy

Abstract

In this article, we provide an overview of machine learning (ML) methods, both classical and deep variants, that are used to implement self-healing for cell outages in cellular networks. Self-healing is a promising approach to network management, which aims to detect and compensate for cell outages in an autonomous way. This technology aims to decrease the expenses associated with the installation and maintenance of existing 4G and 5G, i.e. emerging 6G networks by simplifying operational tasks through its ability to heal itself. We provide an overview of the basic concepts and taxonomy for SON, self-healing, and ML techniques, in network management. Moreover, we review the state-of-the-art in literature for cell outages, with a particular emphasis on ML-based approaches.