Performance Comparison of Reconfigurable Intelligent Surfaces Shapes Using Sionna

Sammanfattning

Future mobile networks like 6G face key challenges, including ultra-reliable, low-latency communications, massive device connectivity, and high data rates. These become increasingly complex in environments with poor signal propagation, leading to inadequate coverage, reduced data rates, and unreliable communication. Reconfigurable Intelligent Surfaces (RIS) have emerged as a promising solution since they can redirect signals and create a virtual Line-of-Sight (LoS), effectively improving coverage and the Signal-to-Noise Ratio (SNR). While most studies focus on two-dimensional (2D) rectangular RIS surfaces, there is no theoretical evidence to suggest that this shape is optimal. An additional challenge arises when the user is in mobility, as the RIS configurations need to be updated to maintain optimal performance. How often these updates should occur remains an open problem. This thesis investigates the effectiveness of various RIS geometries using Sionna, a GPU-accelerated ray tracing simulator developed by Nvidia. The evaluation includes one-dimensional (1D) uniform linear arrays (ULA), 2D uniform planar arrays (UPA), and three-dimensional (3D) shapes. Simulations are conducted across various scenarios, including 3.5 GHz and 28 GHz frequency bands, single and dual-antenna transmitters, and differing mobility conditions. The results indicate that 3D geometries outperform UPA and ULA configurations as they provide higher SNR. Additionally, the study investigates how often the RIS control signaling should be transmitted to change the RIS configuration considering user mobility and demonstrates that transmitting it every two meters of receiver movement is sufficient to maintain high SNR levels. These values correspond to a transmission interval of approximately 1.42 seconds when the receiver walks at an average speed of 1.4 m/s and 0.24 seconds when moving in a car at 30 km/h.