Specific programme: Joint Programming Initiative in “Machine Learning-based Communication Systems, towards Wireless AI (WAI)” funded through CHISTERA IV Cofund 2022 programme and AEI “Proyectos de Cooperación Internacional” (Proyecto PCI2023-145981-2, financiado por MCIN/AEI/10.13039/501100011033 y por la Unión Europea).

APCIN code: PCI2023-145981-2

UPV/EHU Partner Status: Beneficiary

UPV/EHU PI: Eneko Iradier

Project start: 31/12/2023
Project end:   30/12/2026
 

Brief description: Mobile communications have changed, and will continue to change our lives. With 5G under deployment, the interest of the scientific and industrial communities has started focusing on the future 6G communication networks, which will require more advanced capabilities. Achieving new challenging requirements calls for a paradigm shift that PASSIONATE will be advancing.

In the architectural domain, there is a need for full integration and interoperation between satellite, aerial and terrestrial network components, merged in a unique dynamic‐adaptive network infrastructure denoted as the 3D network. Within this architecture, the evolution of mobile communications needs a combination of several innovative and complementary advances at the physical layer (PHY), medium access control (MAC), and radio resource management (RMM) that may be optimised with the use of Artificial Intelligence (AI) and Machine Learning (ML). With these goals in mind, PASSIONATE will unlock ML for wireless by customising and accounting-by-design the unique properties (physics-based) of the networks they are applied to. Physics-based ML is, in addition, the suitable approach to ensure the scalability, generalisation, reliability, and user trust of ML, enabling ML solutions that are technically robust and possibly explainable-by-design.

In PASSIONATE, we will develop the understanding and vision of what the application of AI/ML to the wireless network can provide and design use cases that can take advantage of this technology. For these use cases and with the new physics-based AI/ML tools, we will design new PHY, MAC, and RRM techniques and algorithms that achieve the ambitious goals of future mobile networks regarding coverage, data rate, latency, and energy consumption. We will evaluate experimentally by realistic simulations and measurements the achieved gains and contribute to creating data sets that can be used for the community. By advancing the state of the art and stimulating research and technology-based innovation through dissemination, PASSIONATE will create awareness and facilitate the positive impact of advanced wireless communications on society and the economy.