Low-Complexity Stochastic Power Control for UAV-Aided Multi-Cell mMIMO NOMA Systems

We consider an unmanned aerial vehicle (UAV)-relay-assisted massive multi-input multi-output system, where a base station (BS) serves coverage-limited user equipments (UEs) using non-orthogonal multiple access (NOMA). Due to the UAV altitude, the channels contain both line-of-sight (LoS) and non-LoS components. Moreover, the mobility of UAVs and UEs causes channel aging. We analyse the system spectral efficiency (SE) with minimum mean square error precoding, and by considering practical Rician channels with phase-shifts, channel aging, imperfect successive interference cancellation, and pilot contamination. We also develop an iterative stochastic optimization algorithm to optimize the system global energy efficiency. Our algorithm has extremely low complexity, due to its closed-form power updates, and converges faster than the existing state-of-the-art deterministic algorithm, which numerically calculates the SE.