Enhancing Network Data Analytics Functions: Integrating AIaaS with ML Model Provisioning - Archive ouverte HAL
Communication Dans Un Congrès Année : 2024

Enhancing Network Data Analytics Functions: Integrating AIaaS with ML Model Provisioning

Ali Nadar
  • Fonction : Auteur
  • PersonId : 1264997
Jérôme Härri
  • Fonction : Auteur
  • PersonId : 1202225

Résumé

The Network Data Analytics Function (NWDAF) is a new 5G Core System (5GS) application function providing network analytics via data collection and exposure APIs and predefined data analytics or AI/ML models. 3GPP rel.16 only supports locally trained and inferable AI/ML models, which might be a limiting factor in decentralized, multi-vendors/tenants 5G architectures. 3GPP rel.17 proposes APIs for AI/ML sharing between NWDAF to address these limitations. In this paper, we present a 3GPP rel.17 architecture extending OpenAirInterface (OAI) NWDAF towards AI-as-a-Service and supporting AI/ML model sharing. We integrate an AI/ML ontology for AI/ML and introduce APIs between the NWDAF and an AIaaS platform. We finally demonstrate its feasibility via AI/ML model sharing abnormal traffic behavior use case.
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Dates et versions

hal-04639577 , version 1 (09-07-2024)

Identifiants

Citer

Ali Nadar, Jérôme Härri. Enhancing Network Data Analytics Functions: Integrating AIaaS with ML Model Provisioning. MedComNet 2024, 22nd Mediterranean Communication and Computer Networking Conference, IEEE, Jun 2024, Nice, France. pp.1-4, ⟨10.1109/MedComNet62012.2024.10578162⟩. ⟨hal-04639577⟩

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