Resource Allocation Optimisation in NOMA-Assisted Low-Altitude Economy IoT Caching Networks

Low-altitude economy (LAE) is one of the important scenarios in 6G non-orthogonal multiple access (NOMA)-assisted Internet of Things (IoT) wireless caching networks (WCNs). It is necessary to allow the LAE IoT networks to cache the most popular files at the network edge to reduce the traffic and latency consumption over the backhaul links. In this work, we consider an unmanned aerial vehicle (UAV)-assisted LAE IoT WCN system to minimise the total latency based on content placement phase and content delivery phase models. To overcome the optimisation problem, we propose a genetic algorithm (GA)-based power allocation joint optimisation (GA-PAJO) algorithm among all IoT user devices (UDs) by jointly optimising power allocation at the BS and each UAV. Simulation results demonstrate that our proposed optimisation scheme outperforms the benchmarks in terms of minimising total latency among all IoT UDs.