Anagrafe della ricerca

6GTWINS - Deploying Artificial Intelligence in 6G Network Management using Digital Twins

Durata:
24 mesi (2023 - 2025)
Responsabile scientifico:
Tipo di progetto:
Ricerca Nazionale - PRIN
Ente finanziatore:
MINISTERO (Ministero dell'Università e della Ricerca)
Codice identificativo progetto:
2022MWBFEE
Ruolo PoliTo:
Partner

Abstract

5G/6G systems are well known to go beyond the classical “one-fits-all” paradigm of previous radio mobile network generations. They provide not only the possibility to dynamically create, update and delete custom “private networks” in the form of network slices for supporting special applications with challenging heterogeneous needs, but even to split and to distribute the overall system across various stakeholders and domains. These stakeholders can span from infrastructure or connectivity providers to vertical user/application providers. To support this new paradigm, 5G/6G systems are composed of an ever increasing number of control and management components acting on the different domains, which are able to expose and consume 5G and edge computing resources “as-a-Service.” The inner complexity of this multi-tenant and multi-domain environment is envisaged to be handled by the use of “Intent-Based” APIs, able to abstract and to separate diverse Artificial Intelligence (AI) engines providing automated operations and the reinforcement/optimization of policies for each Stakeholder domain. In this complex and radically new environment, the cascade effect of a change in the optimization policy (or even of a single reconfiguration of resources) by a Stakeholder can be hardly mapped onto the effect produced on the overall ecosystem. In detail, this can arise especially in those cases where AI engines of Stakeholders have partially conflicting policies and objectives, and might trigger potential network instability or performance decay. The 6GTWINS project aims to address this issue by exploiting the concept of Digital Twins and applying it to network automation and orchestration. In particular, the project will design solutions for exploiting Digital Twins for What-If analysis and for speeding up AI/ML training. Obviously, the project is not so ambitious to completely fill the research gap in this complex and multi-facet problem, but to move some first steps towards novel approaches and promising technologies. In fact, the project decided to restrict the application scenario of the studied technologies to a representative use-case, namely a Digital Twin-based network orchestration framework for 5G/6G slicing.

Persone coinvolte

Dipartimenti coinvolti

Partner

  • POLITECNICO DI TORINO
  • POLITECNICO DI TORINO
  • UNIVERSITA' DEGLI STUDI DI CATANIA
  • UNIVERSITA' DEGLI STUDI DI GENOVA
  • UNIVERSITA' DI ROMA "LA SAPIENZA"
  • UNIVERSITA' STUDI TRENTO - Coordinatore

Parole chiave

Settori ERC

PE7_8 - Networks (communication networks, sensor networks, networks of robots, etc.)
PE6_2 - Computer systems, parallel/distributed systems, sensor networks, embedded systems, cyber-physical systems

Obiettivi di Sviluppo Sostenibile (Sustainable Development Goals)

Obiettivo 9. Costruire un'infrastruttura resiliente e promuovere l'innovazione ed una industrializzazione equa, responsabile e sostenibile

Budget

Costo totale progetto: € 233.533,00
Contributo totale progetto: € 199.387,00
Costo totale PoliTo: € 47.893,00
Contributo PoliTo: € 39.660,00