The emerging concept of 3D networks, integrating terrestrial, aerial, and space layers, introduces a novel and complex structure characterized by stations relaying backhaul loads through point-to-point wireless links, forming a wireless 3D backhaul mesh. A key challenge is the strategic placement of aerial platform such as drone base stations (DBSs), considering the locations and service demands of ground nodes and the connectivity to backhaul gateway nodes for core network access. This paper addresses these complexities with a two-fold approach: a novel Agglomerative Hierarchical Clustering (HC) algorithm that optimizes DBS locations to satisfy minimum backhaul adjacency and maximum fronthaul coverage radius requirements; and a Genetic Algorithm (GA) that designs backhaul connections to satisfy the cumulative load across the network and maximize the throughput margin which translates to network resilience to increasing demands. Our results showcase the effectiveness of these algorithms against benchline schemes, offering insights into the operational dynamics of these novel 3D networks.

翻译：三维网络这一新兴概念融合了地面、空中和空间层，形成了以节点间点对点无线链路中继回传负载为特征的复杂新型架构，从而构成无线三维回传网格。核心挑战在于如何根据地面节点的位置与服务需求，以及接入核心网的回传网关节点连通性，对无人机基站等空中平台进行战略部署。本文采用双管齐下的方法应对这些复杂性：其一，提出新型凝聚层次聚类算法，通过优化无人机基站位置满足最小回传邻接距离与最大前传覆盖半径的约束；其二，设计遗传算法构建回传连接，以平衡全网累积负载并最大化吞吐量裕度——该裕度直接体现网络对增长需求的鲁棒性。实验结果验证了所提算法相较于基准方案的有效性，为理解这类新型三维网络的运行机理提供了洞见。