This thesis explores Radio Resource Management (RRM) techniques for Joint Communication and Sensing (JCAS) in beyond-5G/6G networks. JCAS integrates communication and sensing functionalities into a unified network, promising enhanced spectral efficiency, reduced system costs, and improved performance in various applications such as autonomous vehicles and smart cities. However, efficiently managing the dual requirements of communication and sensing in a shared network is a significant challenge, especially in dense, multi-cell environments.
The work presents a novel approach to JCAS by developing advanced resource management algorithms aimed at optimizing communication throughput and sensing accuracy, with a focus on target detection as the primary sensing task. The proposed algorithms encompass topology selection, dynamic node contribution management, and joint scheduling of sensing and communication tasks. Through extensive simulations, we analyze the trade-offs between communication and sensing performance, considering metrics such as the average and 10th percentile user throughput and probability of detection. The results demonstrate the effectiveness of the proposed strategies in managing interference and improving system performance in a cooperative multi-cell JCAS network.
This study contributes to the growing body of research on JCAS by addressing key limitations in existing works, such as the lack of cooperative sensing and the need for real-time dynamic resource allocation algorithms. The findings provide practical insights for network operators and lay the groundwork for future research into more complex JCAS applications and further optimization of resource management techniques.

Reconfigurable Intelligent Surfaces (RIS) are envisioned to become a pivotal transformative technology within the realm of 6G mobile networks. In this study, we introduce three heuristic algorithms designed to optimize radio resource management, ultimately enhancing throughput within a RIS-enhanced mobile network. Our findings demonstrate that the algorithm which holistically optimizes user scheduling, RIS-UE association, cell precoding matrices, and RIS configuration matrices outperforms alternative strategies.

Moreover, our investigation delves into uncovering the most effective applications of RIS. This involves a thorough performance comparison of the algorithms across diverse scenarios, encompassing varying RIS deployment configurations (position and orientation), number of users in a network, and number of RIS elements. Additionally, we model the influence of blocker loss—characterized by blocker presence probability and strength—on throughput performance.

In the wake of our study, it becomes evident that RIS exhibits the most promising potential in scenarios involving MU-MIMO configurations, whether within single-cell or multi-cell layouts, and for both indoor and outdoor user settings. However, for SU-MIMO cases, RIS-induced throughput enhancement manifests exclusively in single-cell layouts, and particularly benefits outdoor users in environments marked by substantial blocker strength.