PhD - Gent | More than two weeks ago
Generative AI, by its nature, has the potential to introduce a degree of autonomy to various tasks and fields, including content generation, autonomous vehicles, game development, code generation, scientific research, content curation, and more. Wireless networks are no exception, as the utilization of generative AI can enable self-evolving wireless networks that can adjust, reconfigure, and optimize their functions according to specific network conditions and user demands. The integration of generative AI into wireless networks will fundamentally transform the way wireless networks are designed and operated today. To be precise, Large Language Models (LLMs), a subfield of generative AI, are envisioned to give rise to self-evolving networks. The multi-modal LLMs trained on various wireless data, including RF signals, images, sound, radar, and more, can be fine-tuned to perform several downstream tasks such as beam management, resource management, power management, modulation selection, environmental monitoring, vital sign monitoring, and others. This innovation will lead to the development of a Wireless Foundation Model, eliminating the need for dedicated AI models for each task and paving the way for the realization of artificial general intelligence (AGI)-enabled wireless networks.
The purpose of this PhD research is to explore Wireless Foundation Models by training multi-modal LLMs on various heterogenous wireless data including RF signals, images, radar, etc. and fine-tune the models to various downstream tasks such as beam management, resource management, power management, modulation selection, and others. The selected PhD candidate will be part of a large IMEC team working on the design and implementation of AI/ML for wireless communications and networks. This is a unique opportunity to develop innovative, multi-disciplinary technology and shape future wireless networks. You will publish your research in top-tier journals and conferences.
Required background: Electrical Engineering, Computer Science, Signal Processing for Communications, Machine Learning and Artificial Intelligence
Type of work: 80% design/modeling/simulation, 10% experimental and 10% literature
Supervisor: Adnan Shahid
Co-supervisor: Steven Latre
Daily advisor: Eli De Poorter, Mamoun Guenach
The reference code for this position is 2024-150. Mention this reference code on your application form.