UAV-Assisted Cooperative & Cognitive NOMA: Deployment, Clustering, and Resource Allocation

Abstract

Cooperative and cognitive non-orthogonal multiple access (CCR-NOMA) has been recognized as a promising technique to overcome spectrum scarcity and massive connectivity issues envisioned in next-generation wireless networks. This paper investigates the deployment of an unmanned aerial vehicle (UAV) as a relay that fairly serves many secondary users in a hot-spot region. The UAV deployment algorithm must jointly account for user clustering, channel assignment, and resource allocation sub-problems. We propose a solution methodology that obtains user clustering and channel assignment based on the optimal resource allocations for a given UAV location. This paper is the first to jointly derive closed-form optimal power and time allocations for generic cluster sizes of CCR-NOMA networks. Derivations consider many practical limitations, such as hardware impairments, imperfect channel estimates, and interference temperature constraints. Compared to numerical benchmarks, proposed solutions reach optimal max-min fair data rate by consuming and spending much less transmission power and computational time. The proposed clustering uses the optimal data rates and channel assignment approaches based on a linear bottleneck assignment (LBA) algorithm. Numerical results show that the LBA achieves 100% accuracy in more than five orders of magnitude less time than the optimal integer linear programming benchmark.