## 3 RIS Modeling and Simulation  
Simulations are essential to designing RISs and predicting their behavior in different scenarios. Next, we provide a detailed overview of simulation tools and techniques used in RIS design. Electromagnetic simulation software, such as CST Microwave Studio (Dassault Systems, Velizy-Villacoublay, France), Ansys's HFSS (Ansys, Canonsburg, PA, USA) and Altair's FEKO (Altair Engineering Inc., Troy, MI, USA, is used to simulate the behavior of electromagnetic waves in RIS structures. These software tools use numerical methods to solve [PERSON]'s equations and predict the electromagnetic field distribution, transmission, reflection, and absorption in RIS structures [36]. Numerical optimization techniques, such as genetic algorithms and particle swarm optimization, can optimize the RIS's design parameters, such as element size, spacing, and shape, for specific performance metrics, such as phase shift range, radiation pattern, and efficiency. These optimization techniques can speed up the design process and improve the overall performance of the RIS [37]. Ray tracing is a simulation technique that models the propagation of electromagnetic waves using ray paths. It is commonly used to simulate the behavior of electromagnetic waves in complex indoor environments where multiple reflections and diffractions occur. Ray tracing software, such as COMSOL (Burlington, MA, USA) and MATLAB (Natick, MA, USA), can predict the signal strength and quality in different scenarios and optimize the RIS design accordingly [38]. Circuit simulation software, such as SPICE (UC Berkeley, Berkeley, CA, USA) and ADS (PathWave Design, Santa Rosa, CA, USA), can model the electronic circuits and control algorithms used in RISs. These software tools can simulate the behavior of electronic components, such as amplifiers, filters, and switches, and optimize the circuit design for the desired performance metrics, such as power consumption and speed [39]. Machine learning techniques, such as deep learning and reinforcement learning, can be used to predict the behavior of RISs in different scenarios and optimize the control algorithms. These techniques can be learned from data and experience to improve the accuracy and efficiency of RIS control [40]. Each simulation approach has its advantages and disadvantages. Electromagnetic simulation software provides accurate and detailed simulation results, but can be computationally intensive, while numerical optimization techniques provide fast optimization results, but may not provide detailed simulation results. Ray tracing is suitable for simulating complex indoor environments, while circuit simulation is suitable for simulating RIS control algorithms. Machine learning provides fast and accurate simulation results, but requires a lot of training data. Therefore, the optimal simulation approach depends on the specific application, the desired performance metrics, and the available resources [40; 41]. Experimental validation is a crucial step in the development of RIS technology. It involves verifying the performance of RIS devices under real-world conditions and comparing the results with theoretical predictions obtained from simulations. The following are some of the experimental validation techniques commonly used for RISs. Antenna measurements are used to verify the radiation characteristics of RIS devices. The RIS device is placed in an anechoic chamber, and the radiation pattern is measured using a calibrated antenna. The measured radiation pattern is compared with the simulation results to validate the RIS's design [42]. Channel sounding involves measuring the wireless channel response between a transmitter and receiver in the presence of RIS devices. The RIS device is placed in the wireless channel, and the channel response is measured using a channel sounder. The measured channel response is compared to the simulation results in order to to validate the RIS's design [43]. Beamforming measurements are used to verify the beamforming capabilities of RIS devices.