Principal Investigator
:
prof. Hanna Bogucka
Poznan University of Technology
Panel: ST7
Funding scheme
: CHIST-ERA
announced on
2 November 2022
In the architectural domain of future 6G communication networks, 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, the project on “Physics-based wireless AI providing scalability and efficiency” (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, generalization, reliability and user trust of ML, enabling ML solutions that are technically robust and possibly explainable-by-design.
prof. Hanna Bogucka, photo Łukasz Bera
In line with the objective of the CHIST-ERA call, the PASSIONATE project will accelerate the path towards relevant Wireless AI by successfully integrating algorithms, software and hardware-oriented proof-of-concepts. By developing novel physics-based AI/ML to optimise the future wireless network, the PASSIONATE project addresses the following topics specified in the call:
- it will design and apply AI/ML-enhanced techniques to the physical layer and resource optimisation of Radio Access Networks, including MIMO processing and beamforming;
- it will also be applied to improving spectrum sensing;
- it will increase the energy efficiency of future radio networks;
- it will develop algorithms for reflective intelligent surfaces; and
- it will provide the trustworthy and reliable physics-based radio AI algorithms.
The combination of software and hardware allows the synthetic data that will be generated for ML training to be validated with measurements, rendering it more reliable and fitted to reality. The data, algorithm descriptions and code will be shared in open access to facilitate the reproducibility of the experiments, and we will contribute to enhancing some of the open access simulators that are already available.
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 challenging new requirements calls for a paradigm shift that the project PASSIONATE will be advancing.
In PASSIONATE, we will develop the vision of what the application of AI/ML to the wireless network can provide. The project’s ambitious goals for coverage, data rate, latency, energy consumption and security will serve future users of the future mobile networks. As a result, PASSIONATE will have a positive impact on society and the economy.
Project title: Physics-based Wireless AI Providing Scalability and Efficiency
prof. Hanna Bogucka
Prof. Dr hab. inż. Hanna Bogucka is a Professor of Technical Sciences and the Director of the Institute of Radiocommunications at Poznan University of Technology. She conducts research on radio technologies. She is the author of more than 200 scientific articles and monographs. She is also a leader of important international bodies. In 2014-15, she was the Director of the IEEE Communications Society (ComSoc) for the Europe-Middle East-Africa region. In 2015-16, she served as the Chair of the IEEE Radio Communications Committee, and she is currently a Board Member of IEEE ComSoc. She is a member of the Polish Academy of Sciences, Vice President of the Poznan branch of the Polish Academy of Sciences and Vice Chair of the Committee on Electronics and Telecommunications of the Polish Academy of Sciences.
