  This article proposes a novel framework for unmaned aerial vehicle (UAV)
networks with massive access capability supported by non-orthogonal multiple
access (NOMA). In order to better understand NOMA enabled UAV networks, three
case studies are carried out. We first provide performance evaluation of NOMA
enabled UAV networks by adopting stochastic geometry to model the positions of
UAVs and ground users. Then we investigate the joint trajectory design and
power allocation for static NOMA users based on a simplified two-dimensional
(2D) model that UAV is flying around at fixed height. As a further advance, we
demonstrate the UAV placement issue with the aid of machine learning techniques
when the ground users are roaming and the UAVs are capable of adjusting their
positions in three-dimensions (3D) accordingly. With these case studies, we can
comprehensively understand the UAV systems from fundamental theory to practical
implementation.
