Minimizing Delay for MIMO-NOMA Resource Allocation in UAV-Assisted Caching Networks
As one of the important enabling techniques for 6G, wireless caching network (WCN) attracts significant attentions. In this paper, we jointly apply unmanned aerial vehicle (UAV), millimeter wave (mmWave) multiple input multiple output (MIMO) and non-orthogonal multiple access (NOMA) in WCN. Our aim is to minimize the user delay, which is decomposed into three sub-problems, i.e., UAV deployment, hybrid beamforming and power allocation. Firstly, to improve the user rate, we apply K-means to reduce the distance between UAVs and users and propose a user pairing method to maintain the channel gain gap in each pair. Then, for increasing UAV hit probability, particle swarm optimization (PSO) and zero forcing are used for analog beamforming and digital beamforming, respectively. Finally, to further improve the user data rate, the genetic algorithm (GA) is applied to calculate the optimal NOMA power allocation factors. Simulation results confirm that the proposed schemes can achieve lower user delay compared with baseline schemes.