Zhu Bo

The primary objectives include exploring and applying appropriate ML techniques for designing compact ISAC systems based on 5G signals and other emerging waveforms anticipated for 6G. Additionally, leveraging ML for the design of novel and adaptable waveforms capable of addressing both communication and sensing requirements. Finally, developing dynamic algorithms based on ML techniques to address various scenarios. The research aims to develop algorithms that are simultaneously flexible and of low complexity to enhance current designs. Given the emphasis on flexibility as a key design approach, these algorithms should adapt to diverse scenarios and use cases, outperforming current designs tailored for specific scenarios and demonstrating improved performance in varying conditions. The study envisions multiple use cases with applied ML techniques, expecting validation through extensive simulations. Furthermore, the research anticipates providing guidelines for applying these techniques to other use cases and scenarios. The most promising techniques identified will be implemented in a demonstrator.