基于无线感知辅助的车联网下行无线资源分配方法

Downlink wireless resource allocation method of V2X based on wireless sensing assistance

在车联网(vehicle-to-everything,V2X)中,感知与安全类数据的高速下发与共享对道路交通安全至关重要。然而,车辆高速移动所引起的通信链路不稳定,会导致现有的基于车辆位置信息上报的通信资源分配方法不再高效。为此,提出了一种基于通信节点无线感知辅助的车联网下行无线资源分配方法。首先,构建了通信与感知资源正交下的通信模式选择与无线资源分配联合优化问题;之后,为解决这一问题提出了基于Delaunay三角划分的分簇通信模式选择与基于改进图着色的资源分配策略,以实现下行吞吐量的提升;最后,仿真分析了无线感知估计误差、车辆数量对算法性能的影响。仿真结果表明,与传统方法相比,所提算法在相同感知带宽资源占比下可获得更好的下行通信性能增益,并能够承受更大的感知误差对性能的影响,同时,所提算法的计算复杂度较低,可节省网络算力资源。

In vehicle-to-everything, the high-speed distribution and sharing of perception and safety data is crucial to road safety. However, the instability of communication links caused by high-speed vehicle movement will make the existing communication resource allocation methods based on vehicle location information reporting no longer efficient. Therefore, a downlink wireless resource allocation method based on wireless sensing assistance of communication nodes was proposed. Firstly, a joint optimization problem of communication mode selection and wireless resource allocation under the orthogonality of communication and sensing resources was constructed. Then, a communication mode selection based on clustering by Delaunay triangulation and a resource allocation strategy based on improved graph coloring to improve the downlink throughput was proposed to solve the problem of construction. Finally, the impact of wireless sensing estimation error and the number of vehicles on the performance of the algorithm was simulated and analyzed. Simulation results show that compared with traditional methods, the proposed algorithm can obtain better downlink communication performance gain under the same proportion of sensing bandwidth resources, and can withstand the impact of larger sensing errors on performance. At the same time, the computational complexity of the proposed algorithm is lower and the network computing resources can be saved.