Fundamental Limits of Integrated Sensing and Communication without Perfect Channel State Information

This thesis explores the theoretical underpinnings and practical implications of Integrated Sensing and Communication (ISAC) within future wireless networks. As a transformative technology, ISAC merges sensing and communication functionalities to optimize the utilization of spectrum and hardware resources, a necessity driven by the increasing demands of modern applications such as autonomous vehicles and immersive technologies. The research addresses the critical need to understand the fundamental trade-offs between sensing and communication within ISAC systems. It investigates the Bayesian Cramér-Rao Bound (BCRB) as a metric for evaluating the performance limits of ISAC systems under various conditions. This bound is particularly relevant given the non-realistic nature of assuming known state information in dynamic channel environments, necessitating continuous estimation. This thesis evaluates the performance of different transmission strategies by developing a comprehensive theoretical model. It balances the conflicting requirements of sensing and communication, thus providing insights into the optimal allocation of power and resources. The simulation results validate the theoretical predictions, demonstrating how ISAC can enhance spectral, energy, and economic efficiencies in next-generation wireless networks. The findings contribute significantly to the field by laying the groundwork for future research to integrate further and optimize sensing and communication. The methodologies and insights presented in this work are expected to drive the development of advanced ISAC solutions, pushing the boundaries of what is achievable in wireless communication systems.