基于灰狼优化和匈牙利算法的D2D资源分配策略

当前，车联网系统中存在频谱资源有限、系统干扰较大、D2D(device to device)用户通信质量差等问题。针对上述情况，提出了一种基于灰狼优化和匈牙利算法的D2D资源分配策略。在保证蜂窝用户以及D2D用户通信质量的前提下，该模型首先利用灰狼优化获得每个D2D用户的最佳发射功率，然后利用匈牙利算法实现D2D用户与蜂窝用户的最佳信道匹配，最大化D2D用户的总容量。仿真结果表明，该算法与和声搜索算法和遗传算法相比，在有效提升D2D用户的总容量同时，单个D2D用户也能获得较高的最大、最小容量。

D2D resource allocation strategy based on gray wolf optimizer and Hungarian algorithm

At present, there are some problems in the Internet of Vehicles system, such as spectrum resources shortage, large system interference, and the communication quality of D2D users. To solve these problems, this paper proposed a D2D resource allocation strategy based on GWO-HA. On the premise of meeting the communication quality of cellular users and D2D users, this model used the gray wolf optimizer(GWO) to get the optimal transmission power of each D2D user. Then it realized the optimal channel matching between DUE and CUE by the Hungarian algorithm(HA) and obtained the maximum total capacity of D2D users. Simulation results show that, compared with harmony search(HS) and genetic algorithm(GA), the proposed algorithm can effectively improve the total capacity of D2D users. At the same time, single D2D user can also obtain high maximum and minimum capacities.