PhD "Radio signal design for ISAC system optimization" F/M

about the role

Your role is to complete a PhD thesis project on: " Radio signal design for ISAC system optimization ".

The PhD candidate will engage in cutting-edge research on waveform design and resource allocation in ISAC systems. As a key enabler of 6G, ISAC allows for efficient wireless resource utilization while simultaneously enhancing data transmission and environmental awareness. Its significance spans various applications, contributing to smarter cities, improved industries, and advanced environmental monitoring.

One of the major challenges in ISAC systems is managing the tradeoffs between communication and sensing performance. Enhancing communication capacity may reduce sensing accuracy and resolution, and vice versa. Addressing these tradeoffs is crucial for optimizing system performance, particularly in balancing communication efficiency with sensing precision. Consequently, the main research areas identified are around the design of the radio signal (modulation, pilots symbols) allowing better resolution and precision in its detection, essential for ensuring ISAC meets the demands of future wireless networks.

The objective of the PhD thesis is to tackle key challenges in ISAC by developing innovative solutions that optimize both sensing and communication tasks. A primary focus will be on radio signal design, particularly based on multi-carrier or spread multicarrier, to enhance ISAC performance while maintaining compatibility with future communication systems. Additionally, the study will investigate the network deployment typology with the number of radio sites to deploy for a better environment prediction, that should have an impact on ISAC efficiency. A critical aspect of this work involves exploring the tradeoffs between communication efficiency and sensing accuracy to develop optimized strategies that strike the right balance. Furthermore, the research will aim to use AI/ML to allow/optimize the radio signal detection to improve the sensing performance and the communication quality. Some AI/ML models will use the sensed radio data to perform prediction of the environment that can be also used to optimize the communication by adapting the radio signal design.

To ensure the practical viability of the proposed methods, rigorous validation through simulations will be conducted (e.g., MATLAB, Sionna (TensorFlow)), assessing their effectiveness in real-world ISAC deployment. The results will contribute to ongoing research in ISAC and may be highlighted in scientific publications and/or patents.