Unmanned aerial vehicles (UAVs) advance 5G and 6G networks with flexible deployment and enhanced coverage. However, their limited onboard energy poses challenges, requiring optimization through advanced energy harvesting, flight planning, and adaptive protocols. On the other hand, Reconfigurable Intelligent Surfaces (RISs) are emerging as a transformative technology for future wireless systems, particularly in the context of 6G for dynamically improving signal strength and coverage. Integrating RISs with UAVs addresses propagation issues, enhancing communication with RISs on buildings or UAVs. However, several critical research areas remain unexplored. These include understanding the complex radio wave propagation characteristics in aerial environments, optimizing the deployment of 3D RIS arrays to maximize their benefits in dynamic scenarios, and ensuring seamless integration of UAVs and RISs into existing and future wireless standards. Overcoming these challenges will be pivotal in harnessing the full potential of UAVs and RISs to meet the demanding requirements of 6G networks, thereby ushering in a new era of pervasive and high-performance wireless connectivity.

This thesis addresses UAV-assisted communication challenges, focusing on energy harvesting and deployment optimization. It models solar, wind, and hybrid energy scenarios, deriving expressions for harvested power to evaluate outage probabilities using moment generating functions (MGF) and Gil-Pelaez inversion. It also explores distributed STAR-RIS networks versus multi-antenna UAV systems, optimizing multi-user scheduling, STAR-RIS shifts, and UAV beamforming using semi-definite programming, integer relaxation, and convex approximation, showing significant performance gains and enhancing UAV efficiency.