AI-Driven UAV-NOMA-MEC in Next Generation Wireless Networks
Zhong Yang, Mingzhe Chen, Xiao Liu, Yuanwei Liu, Yue Chen, Shuguang Cui, H. Vincent Poor
Abstract
Driven by the unprecedented high throughput and low latency requirements anticipated for next generation wireless networks, this article introduces an artificial intelligence (AI)-enabled framework in which unmanned aerial vehicles use non-orthogonal multiple access and mobile edge computing techniques to serve terrestrial mobile users (MUs). The proposed framework enables terrestrial MUs to offload their computational tasks simultaneously, intelligently, and flexibly, thus enhancing their connectivity as well as reducing their transmission latency and energy consumption. In particular, the fundamentals of this framework are first introduced. Then a number of communication and AI techniques are proposed to improve the quality of experience of terrestrial MUs. In particular, 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.