Resource Allocation in V2X Networks: A Double Deep Q-Network Approach with Graph Neural Networks

With the advancement of Vehicle-to-Everything (V2X) technology, efficient resource allocation in dynamic vehicular networks has become a critical challenge for achieving optimal performance. Existing methods suffer from high computational complexity and decision latency under high-density traffic and heterogeneous network conditions. To address these challenges, this study presents an innovative framework that combines Graph Neural Networks (GNNs) with a Double Deep Q-Network (DDQN), utilizing dynamic graph structures and reinforcement learning. An adaptive neighbor sampling mechanism is introduced to dynamically select the most relevant neighbors based on interference levels and network topology, thereby improving decision accuracy and efficiency. Meanwhile, the framework models communication links as nodes and interference relationships as edges, effectively capturing the direct impact of interference on resource allocation while reducing computational complexity and preserving critical interaction information. Employing an aggregation mechanism based on the Graph Attention Network (GAT), it dynamically adjusts the neighbor sampling scope and performs attention-weighted aggregation based on node importance, ensuring more efficient and adaptive resource management. This design ensures reliable Vehicle-to-Vehicle (V2V) communication while maintaining high Vehicle-to-Infrastructure (V2I) throughput. The framework retains the global feature learning capabilities of GNNs and supports distributed network deployment, allowing vehicles to extract low-dimensional graph embeddings from local observations for real-time resource decisions. Experimental results demonstrate that the proposed method significantly reduces computational overhead, mitigates latency, and improves resource utilization efficiency in vehicular networks under complex traffic scenarios. This research not only provides a novel solution to resource allocation challenges in V2X networks but also advances the application of DDQN in intelligent transportation systems, offering substantial theoretical significance and practical value.