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Article Dans Une Revue Communications Surveys and Tutorials, IEEE Communications Society Année : 2024

Machine Reasoning in FCAPS: Towards Enhanced Beyond 5G Network Management

Adlen Ksentini
Christos Verikoukis

Résumé

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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Dates et versions

hal-04600018 , version 1 (04-06-2024)

Identifiants

Citer

Abdelkader Mekrache, Adlen Ksentini, Christos Verikoukis. Machine Reasoning in FCAPS: Towards Enhanced Beyond 5G Network Management. Communications Surveys and Tutorials, IEEE Communications Society, 2024, pp.1-1. ⟨10.1109/COMST.2024.3395414⟩. ⟨hal-04600018⟩
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