Work

Maestro: LLM-Driven Collaborative Automation of Intent-Based 6G Networks

Year: 2024

Type: article

Abstract: This letter presents Maestro, a collaborative framework leveraging Large Language Models (LLMs) for automation of shared networks. Maestroenables conflict resolution and collaboration among stakeholders in a shared intent-based 6G network by abstracting diverse network infrastructures into declarative intents across business, service, and network planes. LLM-based agents negotiate resources, mediated by Maestroto achieve consensus that aligns multi-party business and network goals. Evaluation on a 5G Open RAN testbed reveals that integrating LLMs with optimization tools and contextual units builds autonomous agents with comparable accuracy to the state-of-the-art algorithms while being flexible to spatio-temporal business and network variability.

Source: IEEE Networking Letters

DOI: 10.1109/lnet.2024.3503292

Authors: Ilias Chatzistefanidis, Andrea Leone, Navid Nikaein

Institution: EURECOM

Language: English

Topic: Software-Defined Networks and 5G

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $2,566