Intent-based multi-agent reinforcement learning for service assurance in cellular networks

Perepu, Satheesh K.; Martins, Jean Paulo; Souza, S, Ricardo; Dey, Kaushik

doi:10.1109/globecom48099.2022.10001426

Cited by 7 publications

(1 citation statement)

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“…Furthermore, independent explorations have delved into autonomous management frameworks. Such as employing multi-agent reinforcement learning agents to enforce specified Key Performance Indicators (KPI) [7]. However, few studies have yet to examine the performance of CL systems in real-world application services.…”

Section: Introductionmentioning

confidence: 99%

Closed-Loop Automation in 6G for Minimum Downtime Task Continuity in Surveillance Cobots

Pires,

Blue,

Malinen

et al. 2024

2024 Joint European Conference on Networks and Communications &Amp;amp; 6G Summit (EuCNC/6G Summit)

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This research delves deeply into the integration of collaborative robots (cobots) in the context of advancing 6G technology, aiming to enhance situational awareness. The research underscores the critical importance of Closed-Loop (CL) cobot control within the evolving 6G network architecture, enabling increased automation and improved adaptability. The study provides a comprehensive background on Intent-Based Networks (IBN) and CL mechanisms, introduces an application scenario, and outlines a framework for implementing CL systems in cobot cluster networks. An experimental setup is detailed, and a CL-enabled application migration use-case is demonstrated. This work contributes valuable insights to the ongoing discussion on the evolution of next-generation networks.

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Section: Introductionmentioning

confidence: 99%