  Driven by the unprecedented high throughput and low latency requirements in
next-generation wireless networks, this paper introduces an artificial
intelligence (AI) enabled framework in which unmanned aerial vehicles (UAVs)
use non-orthogonal multiple access (NOMA) and mobile edge computing (MEC)
techniques to service terrestrial mobile users (MUs). The proposed framework
enables the terrestrial MUs to offload their computational tasks
simultaneously, intelligently, and flexibly, thus enhancing their connectivity
as well as reducing their transmission latency and their energy consumption. To
this end, the fundamentals of this framework are first introduced. Then, a
number of communication and AI techniques are proposed to improve the quality
of experiences of terrestrial MUs. To this end, federated learning and
reinforcement learning are introduced for intelligent task offloading and
computing resource allocation. For each learning technique, motivations,
challenges, and representative results are introduced. Finally, several key
technical challenges and open research issues of the proposed framework are
summarized.
