基于多密度流聚类的UAV-NOMA用户分簇与功率分配算法

针对无人机（Unmanned Aerial Vehicle,UAV）辅助非正交多址（Non-Orthogonal Multiple Access,NOMA）下行通信系统,提出了最大化和速率的用户动态分簇与功率分配方案.考虑用户服务质量与UAV位置约束,建立了和速率最大化的优化问题.由于目标函数的非凸性,将原问题解耦为三个子问题,分别优化UAV位置部署与用户连接、用户动态分簇、功率分配以提高系统性能.首先,基于K-means算法设计了UAV位置部署与用户连接方案,以减小路损为目的确定UAV最佳部署位置,同时选择其服务的最优用户群;其次,改进多密度流聚类（Multi-Density Stream Clustering, MDSC）算法,提出了单UAV下用户静态与动态分簇方案,静态分簇方案可自适应平衡簇数与簇用户数,并获得较大的簇内用户信道增益差异,动态分簇方案则针对用户移动属性,制定了即时更新策略;最后,使用分式规划（Fractional Programming,FP）二次变换的方法,引入辅助变量将原非凸问题变换为凸问题,交替优化辅助变量与功率分配因子,获得原非凸问题的次优解.仿真结果表明,与其他算法相比,本文分簇方案能获得更大的簇内信道差异与更小的簇内用户数标准差,同时用户系统性能也获得了显著提升.

User Clustering and Power Allocation Algorithm for UAV-NOMA Based on Multi-Density Stream Clustering

A user dynamic clustering and power alloction scheme is proposed for maximizing the sum rate in a downlink communication system employing non-orthogonal multiple access （NOMA） with unmanned aerial vehicles （UAVs） assistance. Considering the user quality of service and UAV position constraints, an optimization problem is formulated to maximize the sum rate.Due to the non-convexity of the objective function,the original problem is decoupled into three sub-problems to enhance system performance： UAV position deployment and user association, user dynamic clustering, and power allocation to improve system performance. Firstly, a UAV position deployment and user association scheme are designed based on the K-means algorithm, the objective is to minimize path loss, determining the optimal deployment position of the UAV with the simultaneous selection of the optimal user group to be served. Secondly, the multi-density stream clustering （MDSC） algorithm is improved, and a static and dynamic clustering scheme for users under a single UAV is proposed. The static clustering scheme can adaptively balance the number of clusters and the number of cluster users, and obtain a large difference in user channel gain within the cluster. The dynamic clustering scheme formulates an instant update strategy for user mobility attributes.Finally, by applying fractional programming （FP） and quadratic transformation, an auxiliary variable is introduced to transform the original non-convex problem into a convex problem. The auxiliary variable and power allocation are alternately optimized to obtain a suboptimal solution for the original non-convex problem. The simulation results show that compared with other algorithms, the clustering scheme in this paper can obtain larger intra-cluster channel difference and smaller standard deviation of the number of users in the cluster, and the performance of the user system is also significantly improved.