Coverage optimization for 6G using reconfigurable intelligent surfaces

Recent research has shown that a reconfigurable intelligent surface (RIS), which is an attractive technology due to its low complexity and power requirements, can considerably improve the performance of wireless communication systems. RIS composed of small, reconfigurable, passive elements can be programmed to reflect the signal in a particular direction, amplify or attenuate it, or even introduce additional signal components to improve the overall communication performance. RIS can be utilized for different purposes including enhancements to the coverage, positioning, capacity, security, and sustainability. In this thesis, coverage enhancement and the physical layer security (PLS) use cases of RIS are investigated. For the coverage enhancement, single-user and two-user scenarios are discussed. In the single-user scenario, a RIS optimization method using Convolutional Neural Networks (CNN) is proposed to reduce the number of iterations required for optimization. The results show that the proposed CNN model significantly decreases the number of steps required for the configuration by causing a slight degradation in the performance. In the two-user scenario, both coverage enhancement and security use cases are investigated. For coverage enhancement, it is shown that the RIS gain distribution between the users can be managed with respect to the needs by configuring the RIS accordingly. For the security use case, measurement-based characterization of RIS for providing physical layer security, where the transmitter (Alice), the intended user (Bob), and the eavesdropper (Eve) are deployed in an indoor environment is presented. Also, the measurement results are verified with the simulation results. Finally, a thresholding method is proposed to prevent Eve's dominance in PLS and provide more RIS gain to Bob in return for a slight reduction in secrecy capacity.