Date of Award

5-2023

Degree Type

Thesis

Degree Name

Master of Science in Electrical Engineering

Department

Electrical Engineering and Computer Science

First Advisor

Rahmati, Mehdi

Second Advisor

Dong, Lili

Third Advisor

Jenkins, Jaqueline M.

Abstract

This thesis investigates the challenges and opportunities in designing and implementing joint sensing and communication (JSC) systems in vehicle-to-everything (V2X) environments. This emerging concept in vehicular communication connects vehicles to other vehicles, infrastructure, networks, and pedestrians in a smart transportation environment. To address these challenges and seize these opportunities, a new development model is proposed. The primary objective of this work is to enhance the overall performance of the system while leveraging synergies between sensing and communication tasks. To achieve this, advanced methodologies such as the smooth-MUSIC (MUltiple SIgnal Classifcation) algorithm for Angle of Arrival (AoA) estimation, robust beamforming techniques like Capon beamforming, and Extended Kalman Filters (EKF) for state estimation are explored under different conditions. These advanced algorithms offer improved AoA estimation, while the robust beamforming techniques minimize the impact of uncertainties and noise on the system’s performance. A quantitative analysis of the JSC systems’ performance under various noise levels is presented, employing the EKF for state estimation to track moving vehicles, particularly in vehicle-to-infrastructure (V2I) environments. Simulations conducted demonstrate both the impact of uncertainty in sensing on JSC performance and the robustness of the proposed algorithms when noise is considered in the system. In conclusion, this thesis contributes to the growing body of knowledge on JSC, offering valuable insights and practical solutions for enhancing the performance of vehicular networks in the context of connected cars and smart transportation environments.

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