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Article Dans Une Revue IEEE Access Année : 2023

The Hexa-X project vision on Artificial Intelligence and Machine Learning-driven Communication and Computation co-design for 6G

Tamas Borsos
  • Fonction : Auteur
Nandana Rajatheva
  • Fonction : Auteur
Andras Benczur
  • Fonction : Auteur
Hamed Farhadi
  • Fonction : Auteur
Markus Dominik Mueck
  • Fonction : Auteur
Sokratis Barmpounakis
  • Fonction : Auteur
Dilin Dampahalage
  • Fonction : Auteur
Panagiotis Demestichas
  • Fonction : Auteur
Pietro Ducange
  • Fonction : Auteur
Miltiadis C. Filippou
  • Fonction : Auteur
Leonardo Gomes Baltar
  • Fonction : Auteur
Johan Haraldson
  • Fonction : Auteur
Leyli Karaçay
  • Fonction : Auteur
Dani Korpi
  • Fonction : Auteur
Vasiliki Lamprousi
  • Fonction : Auteur
Francesco Marcelloni
  • Fonction : Auteur
Jafar Mohammadi
  • Fonction : Auteur
Nuwanthika Rajapaksha
  • Fonction : Auteur
Alessandro Renda
  • Fonction : Auteur
Mikko A. Uusitalo
  • Fonction : Auteur

Résumé

This paper provides an overview of the most recent advancements and outcomes of the European 6G flagship project Hexa-X, on the topic of in-network Artificial Intelligence (AI) and Machine Learning (ML). We first present a general introduction to the project and its ambitions in terms of use cases (UCs), key performance indicators (KPIs), and key value indicators (KVIs). Then, we identify the key challenges to realize, implement, and enable the native integration of AI and ML in 6G, both as a means for designing flexible, low-complexity, and reconfigurable networks (\textit{learning to communicate}), and as an intrinsic in-network intelligence feature (\textit{communicating to learn }or, 6G as an efficient AI/ML platform). We present a high level description of down selected technical enablers and their implications on the Hexa-X identified UCs, KPIs and KVIs. Our solutions cover lower layer aspects, including channel estimation, transceiver design, power amplifier and distributed MIMO related challenges, and higher layer aspects, including AI/ML workload management and orchestration, as well as distributed AI. The latter entails Federated Learning and explainability as means for privacy preserving and trustworthy AI. To bridge the gap between the technical enablers and the 6G targets, some representative numerical results accompany the high level description. Overall, the methodology of the paper starts from the UCs and KPIs/KVIs, to then focus on the proposed technical solutions able to realize them. Finally, a brief discussion of the ongoing regulation activities related to AI is presented, to close our vision towards an AI and ML-driven communication and computation co-design for 6G.
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Dates et versions

cea-04176728 , version 1 (03-08-2023)

Identifiants

  • HAL Id : cea-04176728 , version 1

Citer

Mattia Merluzzi, Tamas Borsos, Nandana Rajatheva, Andras Benczur, Hamed Farhadi, et al.. The Hexa-X project vision on Artificial Intelligence and Machine Learning-driven Communication and Computation co-design for 6G. IEEE Access, 2023, pp.10.1109/ACCESS.2023.3287939. ⟨cea-04176728⟩
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