Proactive Bandwidth Allocation for V2X Networks With Multi-Attentional Deep Graph Learning

The increasing number of connected vehicles exacerbates the scarcity of spectrum resources in vehicle-to-everything (V2X) communication. To optimize the utilization of wireless resources, it is crucial to allocate the limited spectrum blocks to each roadside unit (RSU) based on the real-time bandwidth demand of vehicles within their coverage. However, the complex mobility patterns of vehicles and dynamic traffic conditions make it challenging to accurately and promptly estimate the bandwidth demand. To address this issue, a spatial-temporal multi-attentional network (STMA-net) is designed to predict the future bandwidth demand of RSUs. Based on the predicted bandwidth demand, a prediction error-compensable proactive bandwidth allocation algorithm is proposed to adaptively allocate spectrum resources and narrow the discrepancy between predicted and actual demand. Experimental results with realistic traffic in Bologna demonstrate that the proposed STMA-net achieves 11.25% higher prediction accuracy compared to state-of-the-art methods. Furthermore, the proposed proactive bandwidth allocation method outperforms existing methods, providing the highest throughput and serving 5% more vehicles while reducing the service drop rate by an order of magnitude.