地空协同场景下加权模糊聚类用户簇划分方法

User cluster partitioning method based on weighted fuzzy clustering in ground-air collaboration scenarios

为了解决应急通信场景下使用无人机作为空中基站进行辅助通信时涉及的无人机基站部署策略中的用户簇划分问题,在兼顾无人机基站性能和用户体验的条件下,提出一种基于特征加权的模糊聚类(Improved FCM)算法。首先,根据每个无人机基站的信号覆盖范围和最大服务用户数量的性能约束,针对随机分布条件下的用户簇在划分过程中算法计算量大不易收敛的问题,提出一种基于距离加权的特征加权节点数据投影算法;其次,针对同一用户处于多个簇有效范围内时用户划分的有效性和无人机基站资源的最大化利用问题,提出一种基于用户位置和无人机基站负载均衡的价值加权算法。实验结果表明,所提方法充分满足无人机基站的服务性能约束,且与几何分形法(GFA)、谱聚类(Sp-C)等算法相比,特征加权模糊聚类算法获得的平均负载率和覆盖比是最优的,分别达到了0.774和0.0263,因此,该算法可为应急通信场景下的用户簇划分问题提供一种可行的解决方案。

To address the user cluster partitioning issue in the deployment strategy of Unmanned Aerial Vehicle(UAV)base stations for auxiliary communication in emergency scenarios,a feature-weighted fuzzy clustering algorithm,named Improved FCM,was proposed by considering both the performance of UAV base stations and user experience.Firstly,to tackle the problem of high computational complexity and convergence difficulty in the partitioning process of user clusters under random distribution conditions,a feature-weighted node data projection algorithm based on distance weighting was introduced according to the performance constraints of signal coverage range and maximum number of served users for each UAV base station.Secondly,to address the effectiveness of user partitioning when the same user falls within the effective ranges of multiple clusters,as well as the maximization of UAV base station resource utilization,a value-weighted algorithm based on user location and UAV base station load balancing was proposed.Experimental results demonstrate that the proposed methods meet the service performance constraints of UAV base stations.Additionally,the deployment scheme based on the proposed methods effectively improves the average load rate and coverage ratio of the system,reaching 0.774 and 0.0263 respectively,which are higher than those of GFA(Geometric Fractal Analysis),Sp-C(Spectral Clustering),etc.