Low-Altitude Economy-Oriented Multi-Layer UAV NOMA Caching Networks: Deployment and Resource Optimization

In this paper, we propose a multi-layer unmanned aerial vehicle (UAV)-assisted non-orthogonal multiple access (NOMA) wireless caching networks (WCNs), where a primary UAV (PUAV) collaborates with multiple secondary UAVs (SUAVs) to enhance service efficiency. Both the PUAV and SUAVs are equipped with caching modules to r educe backhaul dependence. When an SUAV lacks a requested file, it first queries the PUAV, which either transmits the cached file or retrieves it from the g round base station. To minimize user delay, we propose a joint iterative optimization algorithm that integrates user clustering, power allocation, and UAV deployment. First, to ensure balanced clustering, we use Balanced K-Means++ (BKM) to cluster users and form NOMA user pairs by selecting users with good and poor channel conditions. Then, we derive the closed form of power allocation factor to minimize NOMA user pair delay. Additionally, we propose a two-stage particle swarm optimization (PSO) algorithm to iteratively optimize PUAV and SUAV placement. Finally, user pairs and power allocation are refined based on optimized UAV positions. Simulation results show that the proposed scheme achieves the lowest user delay compared to conventional K-Means and fixed SUAV deployment strategies.