Reconfigurable Intelligent Surface (RIS) has emerged as an attractive solution to enhance the performance of next-generation wireless networks. RISs enable dynamic control of electromagnetic wave propagation, making them eligible for realizing Smart Radio Environments (SRE). A RIS is a nearly passive array of multiple reflective antenna elements that dynamically adjust reflection coefficients and phase shifts of the incident wave, enabling real-time, software-controlled manipulation of wave propagation. A key limitation of RIS technology is the need for an integrated gateway with transmit/receive capabilities to receive control and configuration signals, which introduces additional complexity and minimal yet necessary power consumption during configuration. To this end, this thesis investigates the potential of a preprogrammed RIS in wireless networks, aiming to eliminate reliance on an external reconfiguration source while minimizing system complexity and power consumption, thus, improving feasibility for real-world deployment.

First, we propose deploying a preprogrammed RIS eliminating the control link to mitigate 6G signal blockage, while establishing virtual line-of-sight (LoS) channels for improved coverage and data transmission. The preprogrammed RIS sequentially reflects incident signals in directional beams in slotted time resource, allowing the base station to schedule the users to efficiently share a physical resource block (PRB), enhancing coverage and spectral efficiency in obstructed environments. We evaluate the performance gap between the proposed and the conventional RIS architecture and show significant enhancement in coverage even without an external controller linked to the RIS.  

Second, we investigate single-antenna sensor localization in wireless networks using a preprogrammed RIS. We employ dynamic RIS reconfiguration protocols and develop a Maximum Likelihood Estimator for SISO localization. Theoretical analysis, including Fisher Information and Cram\'{e}r-Rao lower bounds, demonstrates significant improvements in localization accuracy. Simulations confirm centimeter-level precision, with the proposed RIS reconfiguration protocol outperforming the sequential beamforming protocol, emphasizing the role of designing novel reconfiguration protocols.

Third, we explore monostatic sensing for passive, preprogrammed RIS-based localization and tracking using a single-antenna full-duplex transceiver. A low-complexity maximum likelihood estimator leverages OFDM signals and RIS phase profiles to mitigate multipath interference. An Extended Kalman Filter (EKF) enhances tracking performance by estimating position and velocity. The proposed method achieves centimeter-level accuracy using just 6 MHz bandwidth, demonstrating robustness in indoor environments with the EKF reducing computational complexity while the RIS being independent of external reconfiguration link.