Abstract

As wireless networks evolve to meet the demands for high-speed, reliable, and secure communications, emerging technologies like Reconfigurable Intelligent Surface (RIS) are set to reshape wireless environments. This thesis investigates the transformative role of RIS in next-generation networks, focusing on performance enhancement and security. The research is divided into two
major contributions.
The first part examines the integration of an active STAR-RIS with a full-duplex Cooperative Rate Splitting Multiple Access (FD C-RSMA) system in a downlink Multiple Input Multiple Output (MISO) configuration. To tackle the non-convex optimization problem, an alternating optimization
framework based on successive convex approximation (SCA) is developed, achieving up to a 20.3% improvement in network sum rate. Additionally, to simplify real-time decision-making, a deep
reinforcement learning model using an actor-critic architecture is proposed, reducing computational time by 98% compared to the SCA method.
The second part explores the effects of a movable antenna (MA)-assisted jammer in a downlink MISO system. The analysis shows that MA-based jamming causes a 30% reduction in sum rate and a 25% increase in outage probability compared to fixed antenna setups. Furthermore, the study evaluates the effectiveness of RIS as a countermeasure against such adversarial attacks under different levels of jammer knowledge. Safeguarding RIS channel state information is found to be critical, as its compromise renders the system ineffective.
Overall, this research provides a comprehensive framework for utilizing RIS to enhance communication performance and strengthen security in wireless environments, laying the foundation for robust next-generation networks.