Resource Allocation for Vehicular Communication Systems

Smart cities utilize real-time data to enhance mobility, connectivity, and safety services. Intelligent transportation systems (ITS) play a crucial role in this endeavor, striving to innovate, plan, operate, evaluate, and manage transportation systems through advanced information and communication technologies. Among these technologies, vehicle-to-everything (V2X) communication systems serve as key enablers for ITS. A leading wireless communication technology to realize V2X communications is the cellular-V2X (C-V2X) network leveraging wireless cellular networks for vehicular communications. However, the performance of C-V2X systems is significantly influenced by resource allocation challenges, including radio resources such as spectrum, transmission power, and energy consumption. This dissertation aims to design resource allocation schemes for vehicular communications in C-V2X networks. Given the limited resources and unique characteristics of vehicular communication systems, such as high vehicle mobility and diverse quality of service (QoS) requirements, the study explores techniques like full-duplex (FD) communications, frequency reuse (FR), and integration of unmanned aerial vehicles (UAVs) to enhance system efficiency. The dissertation addresses five challenges in vehicular communication within C-V2X networks. In the first problem, we address the issue of resource allocation in C-V2X networks, where both cellular and vehicular users contend for limited resources while possessing distinct QoS requirements. Subsequently, the focus narrows to vehicular users exclusively to enhance system efficiency considering resource constraints. The second problem centers on enhancing spectral efficiency in C-V2X networks through FD and FR techniques. In the third problem, the deployment of UAVs to provide communication services to vehicular users in intersection scenarios is explored, offering an energy-efficient resource allocation scheme. Moving forward, the fourth problem investigates the deployment of multiple UAVs in highway scenarios, comparing hovering and flying deployment strategies in terms of resource allocation efficiency. Finally, the fifth problem combines FD and UAV technologies, deploying an FD UAV to provide communication services to a group of vehicular users on a highway, presenting a comprehensive resource allocation policy. For each problem, the dissertation presents the system model and mathematically formulates the resource allocation problem as an optimization problem. Mathematical optimization techniques are employed to address problems one to four, while problem five utilizes reinforcement learning. Simulation results are utilized to evaluate the performance of the proposed schemes.