The development of mobile communication technologies beyond 5G tries to achieve strict requirements, such as ultra-high data rates, reduced latency, high reliability, and extensive coverage. A rising technology is Reconfigurable Intelligent Surfaces (RIS), which can ma- nipulate electromagnetic waves to improve signal quality without the need for additional Base Stations (BSs). This thesis studies the feedback control signaling required for op- timal RIS configuration, focusing on minimizing the signaling overhead while maximizing Signal-to-Noise Ratio (SNR). Two methods are explored: Phase-Shift Quantization and Codebook-Based methods. The Phase-Shift Quantization method studies the impact of discrete phase shifts on each of the elements of the RIS on SNR, demonstrating that with increased resolution, the SNR performance improves. On the other hand, the Codebook-Based method predefines a set of configurations of the RIS, known as the codebook, to reduce the feedback overhead. An optimized version of the Codebook method is also developed, which takes into account a typical position of BS and User Equipment (UE) in mobile networks, reducing the codebook size and improving efficiency. The Rician fading model is also studied in the thesis, to simulate more realistic urban com- munication environments, comparing its impact on SNR with the traditional Line-of-Sight (LoS) scenario. While the presence of multipath components in Rician fading scenarios increases the dispersion in the SNR values, the RIS still performs sufficiently well. The results show the importance of making control signaling as efficient as possible to achieve an optimal balance between maximizing SNR and minimizing resource usage, which is the trade-off studied in this thesis.

Outgoing