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.

In light of the tremendous possibilities offered by waveform design in the near field, we develop simulations of a sub-THz radar system that senses using Airy beams. Remarkably, this class of waveforms is “self-accelerating”: they trace out parabolic trajectories in free space in the absence of external forces. As such, a radar equipped with such a beam should be capable of around-the-corner sensing. This newfound capability opens entirely new dimensions in traffic sensing (among others), and its deployment in traffic radar may lead to significantly improved safety outcomes. Our central contribution lies in determining that conjugating the complex E-field incident on an obstacle (via an active surface) generates an Airy beam that returns to the transceiver plane by tracing the path taken by the original. This is true regardless of obstacle orientation. We also investigated the effect of (vertical) obstacle length on the link-budget. As expected, the percentage of returned power increases with obstacle length, reaching a plateau at ~ 80%. We determine that an ideal obstacle length might be in the vicinity of 0.4 (twice the aperture size of 0.2), since it provides a robust middle ground between returned power and size. In the same line of inquiry, we also found that the smallest obstacle length should be about 0.05 (a quarter of the aperture size). At this limit, an (unconjugated) specular reflection might, in fact, yield greater power returns in the primary lobe than conjugation.