Machine Learning in FCAPS: Toward Enhanced Beyond 5G Network Management

IF 34.4 1区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Communications Surveys and Tutorials Pub Date : 2024-04-30 DOI:10.1109/COMST.2024.3395414

Abdelkader Mekrache;Adlen Ksentini;Christos Verikoukis

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引用次数: 0

Abstract

The increasing complexity of telecommunication networks has highlighted the need for robust network management frameworks. One such framework is FCAPS, which encompasses a wide range of functionalities, including fault management, configuration management, accounting management, performance management, and security management. To effectively address the complexities of modern networks, the integration of Artificial Intelligence (AI) techniques, particularly Machine Learning (ML) and Machine Reasoning (MR), has emerged as a pivotal strategy within FCAPS. ML provides networks with data-driven algorithms to recognize patterns and make informed predictions, while MR focuses on developing understandable AI systems that draw conclusions based on explicit knowledge. In this paper, we explore the field of MR and its usage within FCAPS. First, we present an overview of the FCAPS framework, including a categorization of FCAPS levels. Then, we provide a novel taxonomy of MR approaches, presenting both traditional and advanced MR. Next, we review MR techniques to address emerging concerns within FCAPS. Finally, we discuss open issues and future directions for further study toward 6G networks.

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FCAPS 中的机器推理：面向增强型 5G 网络管理

电信网络的复杂性日益增加，突出表明了对强大的网络管理框架的需求。FCAPS 就是这样一个框架，它包含故障管理、配置管理、会计管理、性能管理和安全管理等多种功能。为有效应对现代网络的复杂性，人工智能（AI）技术，特别是机器学习（ML）和机器推理（MR）的集成已成为 FCAPS 的一项关键战略。机器学习（ML）为网络提供了数据驱动的算法，以识别模式并做出明智的预测，而机器推理（MR）则侧重于开发可理解的人工智能系统，根据明确的知识得出结论。在本文中，我们将探讨 MR 领域及其在 FCAPS 中的应用。首先，我们概述了 FCAPS 框架，包括 FCAPS 级别的分类。然后，我们提供了一种新的磁共振方法分类法，介绍了传统和先进的磁共振方法。接下来，我们回顾了 MR 技术，以解决 FCAPS 中新出现的问题。最后，我们讨论了面向 6G 网络的开放问题和未来研究方向。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Communications Surveys and Tutorials COMPUTER SCIENCE, INFORMATION SYSTEMS-TELECOMMUNICATIONS

CiteScore

80.20

自引率

2.50%

发文量

审稿时长

6 months

期刊介绍： IEEE Communications Surveys & Tutorials is an online journal published by the IEEE Communications Society for tutorials and surveys covering all aspects of the communications field. Telecommunications technology is progressing at a rapid pace, and the IEEE Communications Society is committed to providing researchers and other professionals the information and tools to stay abreast. IEEE Communications Surveys and Tutorials focuses on integrating and adding understanding to the existing literature on communications, putting results in context. Whether searching for in-depth information about a familiar area or an introduction into a new area, IEEE Communications Surveys & Tutorials aims to be the premier source of peer-reviewed, comprehensive tutorials and surveys, and pointers to further sources. IEEE Communications Surveys & Tutorials publishes only articles exclusively written for IEEE Communications Surveys & Tutorials and go through a rigorous review process before their publication in the quarterly issues. A tutorial article in the IEEE Communications Surveys & Tutorials should be designed to help the reader to become familiar with and learn something specific about a chosen topic. In contrast, the term survey, as applied here, is defined to mean a survey of the literature. A survey article in IEEE Communications Surveys & Tutorials should provide a comprehensive review of developments in a selected area, covering its development from its inception to its current state and beyond, and illustrating its development through liberal citations from the literature. Both tutorials and surveys should be tutorial in nature and should be written in a style comprehensible to readers outside the specialty of the article.