RECONFIGURABLE INTELLIGENT SURFACES (RIS): Localization of Users in mmWave/THz Communication Environments

Abstract

In Wireless Communication System, Line-of-Sight (LOS) paths between the transmitter and receiver are often obstructed by trees, walls, buildings, and other obstacles. Reconfigurable Intelligent Surfaces (RIS) offer a promising solution by capturing these blocked signals and intelligently reflecting them toward the intended User Equipment (UE). In such scenarios, the user may be stationary or mobile, and there may be one or multiple users communicating with the same base station (BS). Accurate signal reflection relies on knowing the user’s angle, which is often unknown due to environmental blockages or mobility. To address this, I developed two beam training methods - Exhaustive and Hierarchical - to localize a single static user. Additionally, I proposed two scalable techniques - a correlation-based compressive sensing method and a neural network regression model - to track multiple users simultaneously, whether fixed or mobile. These techniques are shown to be effective in both noiseless and noisy environments, with real-world relevance in Vehicle-to-Everything (V2X) communication, IoT, and mobile networks.