University of Oulu

M. Merluzzi et al., "The Hexa-X Project Vision on Artificial Intelligence and Machine Learning-Driven Communication and Computation Co-Design for 6G," in IEEE Access, vol. 11, pp. 65620-65648, 2023, doi: 10.1109/ACCESS.2023.3287939

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

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Author: Merluzzi, Mattia1; Borsos, Tamás2; Rajatheva, Nandana3;
Organizations: 1CEA, Leti, University of Grenoble Alpes, Grenoble, France
2Ericsson Research, Budapest, Hungary
3Centre for Wireless Communications, University of Oulu, Oulu, Finland
4ELKH Institute for Computer Science and Control, Budapest, Hungary
5Ericsson Research, Stockholm, Sweden
6Institute of Research & Technology (b<>com), Cesson-Sévigné, France
7CNRS, IETR-UMR 6164, INSA Rennes, Université de Rennes, Rennes, France
8Intel Deutschland GmbH, Munich, Germany
9WINGS ICT Solutions, Athens, Greece
10Department of Information Engineering, University of Pisa, Pisa, Italy
11Intel Deutschland GmbH, Munich, Germany Nokia Bell Labs, Munich, Germany
12Ericsson Research, Istanbul, Turkey
13Nokia Bell Laboratories, Espoo, Finland
14Nokia Bell Laboratories, Stuttgart, Germany
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 5 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2023
Publish Date: 2023-09-13


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 ( learning to communicate ), and as an intrinsic in-network intelligence feature ( 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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Series: IEEE access
ISSN: 2169-3536
ISSN-E: 2169-3536
ISSN-L: 2169-3536
Volume: 11
Pages: 65620 - 65648
DOI: 10.1109/ACCESS.2023.3287939
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was supported in part by the European Commission through the H2020 Project Hexa-X under Grant 101015956.
EU Grant Number: (101015956) Hexa-X - A flagship for B5G/6G vision and intelligent fabric of technology enablers connecting human, physical, and digital worlds
Copyright information: © The Author(s) 2023. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see