The Hexa-X Project Vision on Artificial Intelligence and Machine Learning-Driven Communication and Computation Co-Design for 6G

Abstract

We provide 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 provide 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 specific 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 ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">learning to communicate</i> ), and as an intrinsic in-network intelligence feature ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">communicating to learn</i> 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 (FL) 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. 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.