Cross-Layer Resource Allocation for UAV-Assisted Wireless Caching Networks With NOMA

Abstract

Unmanned aerial vehicle (UAV) assisted wireless caching networks (WCN) have been recognized as a promising way to reduce the network load and improve the energy efficiency in the sixth generation (6 G) communication systems. Aiming to improve spectrum efficiency and system capacity, we apply non-orthogonal multiple access (NOMA) in UAV-assisted WCN to serve multiple users on the same spectrum simultaneously and propose the cross-layer resource allocation strategy including the scheduling of UAVs, the grouping of users, and the allocation of power. First, the ρ-K-means algorithm is proposed to assign users to multiple clusters and deploy UAVs according to the distance from UAVs to the base station in the UAV deployment layer. Then, the base station broadcasts the popular files to UAVs via NOMA in the content placement layer. Based on the existing fixed power allocation strategy, we propose a statistic quality of service (QoS) based fixed (SQF) power allocation method to take the statistic QoS of the popular files into consideration and improve the energy efficiency through introducing the discount factor. On the basis of SQF, an instantaneous QoS based adaptive (IQA) strategy allocates power according to the instantaneous QoS of the popular files to reduce the file outage probability. Furthermore, we propose an improved method that is a cross-layer based optimal (CLO) power allocation strategy to maximize the system hit probability. Finally, in the content delivery layer, users in each cluster are grouped according to the channel gain from users to UAVs. In addition, each UAV serves two users on the same time-frequency resource block based on the cognitive radio inspired power allocation for the NOMA user pairs. Simulation results confirm that the proposed ρ-K-means algorithm and CLO strategy reduce the file outage probability and improve the hit probability.