Hierarchical Meta-Reinforcement Learning for Uncertainty-Aware Resource Allocation in C-V2X Networks

Ensuring smooth data interchange between vehicles and infrastructure depends on the smart use of communication resources, including bandwidth, power, and time slots. The framework must respond to user specific requirements, adjust to changing network conditions, and maximize efficiency while minimizing interference. Nevertheless, it has several challenges, such as limited resources, excessive energy consumption, and delays caused due to frequent topological changes by vehicle mobility. This paper proposes the Hierarchical Meta Reinforcement Learning framework based on Information Volume Evidential Markov Decision Processes to address these challenges. Unlike conventional reinforcement learning techniques, this methodology uses hierarchical reinforcement learning to make adaptive decisions in challenging situations. Additionally, by utilizing previously learned policies, it uses meta-learning to facilitate quick tasks adaption, increasing operational efficiency and flexibility. The proposed method achieves a highest V2V link success probability of 98% for 100 vehicles outperforming other techniques like MA-DDQN.