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

Explainable AI in 6G O-RAN: A Tutorial and Survey on Architecture, Use Cases, Challenges, and Future Research

Bouziane Brik
Hatim Chergui
Lanfranco Zanzi
Francesco Devoti
Adlen Ksentini
Muhammad Shuaib Siddiqui
  • Fonction : Auteur
Xavier Costa-Pérez
Christos Verikoukis

Résumé

The recent O-RAN specifications promote the evolution of RAN architecture by function disaggregation, adoption of open interfaces, and instantiation of a hierarchical closed-loop control architecture managed by RAN Intelligent Controllers (RICs) entities. This paves the road to novel data-driven network management approaches based on programmable logic. Aided by Artificial Intelligence (AI) and Machine Learning (ML), novel solutions targeting traditionally unsolved RAN management issues can be devised. Nevertheless, the adoption of such smart and autonomous systems is limited by the current inability of human operators to understand the decision process of such AI/ML solutions, affecting their trust in such novel tools. eXplainable AI (XAI) aims at solving this issue, enabling human users to better understand and effectively manage the emerging generation of artificially intelligent schemes, reducing the human-to-machine barrier. In this survey, we provide a summary of the XAI methods and metrics before studying their deployment over the O-RAN Alliance RAN architecture along with its main building blocks. We then present various use cases and discuss the automation of XAI pipelines for O-RAN as well as the underlying security aspects. We also review some projects/standards that tackle this area. Finally, we identify different challenges and research directions that may arise from the heavy adoption of AI/ML decision entities in this context, focusing on how XAI can help to interpret, understand, and improve trust in O-RAN operational networks.

Fichier principal
Vignette du fichier
publi-8008.pdf (2.05 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Licence

Dates et versions

hal-05045298 , version 1 (24-04-2025)

Licence

Identifiants

Citer

Bouziane Brik, Hatim Chergui, Lanfranco Zanzi, Francesco Devoti, Adlen Ksentini, et al.. Explainable AI in 6G O-RAN: A Tutorial and Survey on Architecture, Use Cases, Challenges, and Future Research. Communications Surveys and Tutorials, IEEE Communications Society, 2024, pp.1-1. ⟨10.1109/COMST.2024.3510543⟩. ⟨hal-05045298⟩

Collections

92 Consultations
1102 Téléchargements

Altmetric

Partager

  • More