Resource Allocation for Platoon Oriented Vehicular Communications: A Neural Network Approach

By driving vehicles in constant spacing, platooning is a promising way to enable a safer and faster mobility with higher lane capacity and energy efficiency. On one hand, recent advances in vehicular communication technologies improve the usefulness of platooning. On the other hand, the string instability in platooning is easily created by the unavoidable communication delays. In this paper, we focus on improving the performance of intraplatoon communications by considering the impact of non-line-of-sight (NLOS), which is typically isolated in most of present works. To do that, we first evaluate the impact of NLOS due to the vehicles as obstacles on signal attenuation among platoon members by developing an analytical mode based on the knife-edge model. To obtain the optimal communication performance, an power control problem is formulated by considering NLOS and multi-user interference. By resorting to the graph neural network (GNN), which can achieve an excellent performance in learning dynamic graph characteristics, an efficient power control policy is developed after modeling the inter-vehicle communication links in the platoon as a fully connected interference graph. Extensive simulations finally validate the performance of our method.