Research Miguel Camelo Botero

Abstract

The overarching objective of 6G-TWIN is to provide the foundation for the design, implementation and validation of an AI-native reference architecture for 6G systems that incorporates Network Digital Twins (NDT) as a core mechanism for the end-to-end, realtime optimisation, management and control of highly dynamic and complex network scenarios. To achieve this objective, 6G-TWIN will deliver methods, modelling and simulation solutions for the definition, creation and management of multi-layered virtual representations of future 6G systems, where heterogeneous domains (i.e., edge, fog and cloud) and communication technologies (e.g., cellular, optical and Non-Terrestrial Networks (NTN)) coexist. The project solutions will be demonstrated in two complementary use cases addressing mobility and energy-efficiency challenges, aligned with the expected use cases of 6G and the Key Performance Indicators (KPI) defined in previously funded projects (including SNS JU STREAM-C/D-2022). Finally, the participation of Small and Medium-sized Enterprises (SMEs) will ensure that the 6G-TWIN consortium pays particular attention to the replication, reengineering and exploitation of the project outcomes, regularly aligning the requirements of standardisation bodies with predicted market needs.

Researcher(s)

• Promoter: Famaey Jeroen
• Co-promoter: Camelo Botero Miguel

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

DAEMON - The success of Beyond 5G (B5G) systems will largely depend on the quality of the Network Intelligence (NI) that will fully automate network management. Artificial Intelligence (AI) models are commonly regarded as the cornerstone for NI design; indeed, AI models have proven extremely successful at solving hard problems that require inferring complex relationships from entangled and massive (e.g., traffic) data. However, AI is not the best solution for every NI task; and, when it is, the dominating trend of plugging 'vanilla' AI into network controllers and orchestrators is not a sensible choice. Departing from the current hype around AI, DAEMON will set forth a pragmatic approach to NI design. The project will carry out a systematic analysis of which NI tasks are appropriately solved with AI models, providing a solid set of guidelines for the use of machine learning in network functions. For those problems where AI is a suitable tool, DAEMON will design tailored AI models that respond to the specific needs of network functions, taking advantage of the most recent advances in machine learning. Building on these models, DAEMON will design an end-to-end NInative architecture for B5G that fully coordinates NI-assisted functionalities. The advances to NI devised by DAEMON will be applied in practical network settings to: (i) deliver extremely high performance while making an efficient use of the underlying radio and computational resources; (ii) reduce the energy footprint of mobile networks; and (iii) provide extremely high reliability beyond that of 5G systems. To achieve this, DAEMON will design practical algorithms for eight concrete NI-assisted functionalities, carefully selected to achieve the objectives above. The performance of the DAEMON algorithms will be evaluated in real-world conditions via four experimental sites, and at scale with data-driven approaches based on two nationwide traffic measurement datasets, against nine ambitious yet feasible KPI targets.

Researcher(s)

• Promoter: Marquez-Barja Johann
• Co-promoter: Camelo Botero Miguel
• Co-promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project website

Project type(s)

• Research Project