| dc.description.abstract | This dissertation addresses the significant challenges of resource allocation in New Radio (NR) Vehicle-to-Everything (V2X) communications within 5G and beyond networks. The rapid evolution of these networks, highlighted in 3GPP Release 15 and beyond, necessitates robust solutions to support critical safety messages and advanced applications that demand high data rates, reliability, spectrum efficiency, and low latency. The proposed solution is a scalable multi-connectivity spectrum management approach based on Multi-Agent Reinforcement Learning (MARL), extending the Multi-Agent Actor-Critic (MAAC) model for NR V2X communications. This approach enhances throughput, success delivery rates, and spectrum efficiency while reducing latency and interference in complex, dynamic environments.
Our proposed algorithms adapt to large-scale systems by accommodating various agents, reflecting the nature of V2X communication. Transformer mechanisms help generalize models to varying environments, improving validation capacity and total utility. Clustering the network into smaller collaboration regions reduces the state spaces, demonstrating scalability by minimizing decision delays and efficiently handling dynamic changes through cooperative and transfer learning. These solutions effectively manage the complexities of V2X environments, minimizing communication overhead and decision-making complexity and utilizing shared observations to enhance practicality in large-scale deployments.
Leveraging transformer architectures, this approach satisfies scalability requirements by training on a small-scale map in a centralized manner and testing on different large-scale maps with varying numbers of agents and Roadside Units (RSUs) in both centralized and decentralized manners. A Gated Recurrent Unit (GRU) layer is also introduced to support agent communication and optimize system performance. This innovative combination enhances learning efficiency and decision-making, enabling collaborative learning and policy sharing among agents.
The first research question addresses the design of a scalable V2X resource allocation architecture that optimizes system throughput, spectrum efficiency, and packet delivery reliability in shared and dynamic environments. This is achieved by developing a Partially Observable Networked Markov Decision Process (MDP) model for distributed multi-connectivity management. The model considers overall system throughput, utility, spectrum efficiency, and packet delivery reliability of V2I, V2S, and V2V links with resource limitations. Evaluations using the Simulation of Urban Mobility (SUMO) platform demonstrated that this model significantly improves throughput, success delivery rates, and spectrum efficiency while reducing latency and interference.
The second research question explores advanced optimization and state estimation techniques to ensure robust resource management and high performance in dynamic V2X environments. This involves utilizing Partial Lagrange multipliers to transform complex optimization problems into reward-based systems and implementing transformer-based state prediction with an additional prediction layer. These techniques accurately forecast the full system state, improving scalability and state estimation in complex and dynamic scenarios. The proposed methods efficiently manage computational complexity and memory requirements, ensuring robust resource management and high performance.
The third research question examines how enhanced agent collaboration mechanisms and the integration of satellite and vehicular networks can improve network reliability and expand connectivity in V2X systems. Innovative collaboration mechanisms are introduced, enabling agents to share experiences and policies more effectively, thus enhancing both learning efficiency and decision-making quality. Additionally, integrating satellite and vehicular networks improves network reliability and expands connectivity, facilitating seamless communication across diverse areas. The results show that these mechanisms significantly enhance the performance and reliability of V2X systems.
This dissertation provides a comprehensive solution to resource allocation challenges in V2X communications. By integrating satellite networks, utilizing advanced transformer architectures, and employing multi-agent reinforcement learning, the proposed approach significantly enhances the performance and reliability of V2X systems. This research lays the groundwork for future advancements in V2X technology, contributing to developing more intelligent and efficient transportation systems. | en_US |