Metraipolis › archive › dc_ea56537fdf56

Toward Fully Autonomous 6G Networks: AI-driven Operational Efficiency and Optimization — arXiv:2609.08426

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.08426
License (as archived): CC0-1.0
Archived: 2026-10-16T14:09:29.051Z
SHA-256 of the archived text: a7d7d8e42ec84dfb5a92a92ac157406c7396cce8db8e77dd611bc01ad6da68d6
Stable URL of this page: https://metraipolis.com/documento/dc_ea56537fdf56

How to cite

Toward Fully Autonomous 6G Networks: AI-driven Operational Efficiency and Optimization — arXiv:2609.08426 — arXiv (en), https://arxiv.org/abs/2609.08426 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ea56537fdf56 (Metraipolis archive dc_ea56537fdf56, sha256 a7d7d8e42ec84dfb5a92a92ac157406c7396cce8db8e77dd611bc01ad6da68d6)

BibTeX

@misc{dcea56537fdf56,
  title = {Toward Fully Autonomous 6G Networks: AI-driven Operational Efficiency and Optimization — arXiv:2609.08426},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.08426},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ea56537fdf56; sha256 a7d7d8e42ec84dfb5a92a92ac157406c7396cce8db8e77dd611bc01ad6da68d6},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.08426. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Mobile networks evolution is characterized by a substantial increase in system complexity, driven by the need to accommodate a growing number of heterogeneous services on top of the digital infrastructure. This growth in service accommodation is expected to accelerate with the adoption of the Network as a Service (NaaS) paradigm, which has emerged as a promising approach to accelerate network innovation while enabling new revenue streams for operators. Although it is fundamental to abstract network capabilities for third-party developers, it poses significant challenges in terms of efficient network operation. To address this increased complexity, future mobile networks are envisioned to be inherently Artificial Intelligence (AI)-native. In particular, the integration of AI within the Radio Access Network (RAN) becomes a key enabler for optimizing operation, energy consumption, and autonomous network control. In this context, this research explores the convergence of AI-native RAN and NaaS ecosystems to enable autonomous 6G RAN management. We propose an Agentic-based orchestration framework capable of interpreting intent-based policies. The proposed framework becomes key to integrate external NaaS requests with internal network management policies.

Tema degli esploratori: calcolo e infrastrutture (retro-catalogo arXiv: annunci del 2026-09-09 in cs.NI).
Categorie arXiv: Networking and Internet Architecture (cs.NI).
Pagina del lavoro: https://arxiv.org/abs/2609.08426 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T13:58:54.250Z dalla pagina https://arxiv.org/catchup/cs.NI/2026-09-09?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).