This paper presents a fully automated, data-driven framework for the large-scale deployment of reconfigurable intelligent surfaces (RISs) in cellular networks. Leveraging physically consistent ray tracing and empirical data from a commercial deployment in the UK, the proposed method jointly optimizes RIS placement, orientation, configuration, and base station beamforming in dense urban environments across frequency bands (corresponding to 4G, 5G, and a hypothetical 6G system). Candidate RIS locations are identified via reflection- and scattering-based heuristics using calibrated electromagnetic models within the Sionna Ray Tracing (RT) engine. Outage users are clustered to reduce deployment complexity, and the tradeoff between coverage gains and infrastructure cost is systematically evaluated. It is shown that achieving meaningful coverage improvement in urban areas requires a dense deployment of large-aperture RIS units, raising questions about cost-effectiveness. To facilitate reproducibility and future research, the complete simulation framework and RIS deployment algorithms are provided as opensource software.

This study presents anisotropic space-time digital metasurfaces designed for high-speed, efficient mathematical operations on compact surfaces. The reconfigurable meta-particles, controlled through adjustable gate biasing of an indium tin oxide (ITO) thin layer, enable real-time, independent control of two polarizations. This processor provides substantial analog signal processing including second order differentiation and edge detection, making it valuable for future integrated optical image processing systems.

Reconfigurable intelligent surfaces offer potential opportunity for wireless signal manipulation. In Power Domain Non-Orthogonal Multiple Access (P-D NOMA), ensuring users receive specific power from the base station is vital. Our paper introduces a semi-analytical method using unequal coefficients superposition to derive closed-form formulas for estimating the beam directivity in large RISs. Employing simplifications to achieve these formulas, we validate their accuracy via full-wave simulations.