A novel framework is proposed for quality of experience (QoE)-driven
deployment and dynamic movement of multiple unmanned aerial vehicles (UAVs).
The problem of joint non-convex three-dimensional (3D) deployment and dynamic
movement of the UAVs is formulated for maximizing the sum mean opinion score
(MOS) of ground users, which is proved to be NP-hard. In the aim of solving
this pertinent problem, a three-step approach is proposed for attaining 3D
deployment and dynamic movement of multiple UAVs. Firstly, genetic algorithm
based K-means (GAK-means) algorithm is utilized for obtaining the cell
partition of the users. Secondly, Q-learning based deployment algorithm is
proposed, in which each UAV acts as an agent, making their own decision for
attaining 3D position by learning from trial and mistake. In contrast to
conventional genetic algorithm based learning algorithms, the proposed
algorithm is capable of training the direction selection strategy offline.
Thirdly, Q-learning based movement algorithm is proposed in the scenario that
the users are roaming. The proposed algorithm is capable of converging to an
optimal state. Numerical results reveal that the proposed algorithms show a
fast convergence rate after a small number of iterations. Additionally, the
proposed Q-learning based deployment algorithm outperforms K-means algorithms
and Iterative-GAKmean (IGK) algorithms with a low complexity.

该论文提出了一个新的框架，用于质量体验驱动的多个无人机的部署和动态移动，并使用遗传算法 K-means 算法和 Q-learning 算法实现了解决非凸 3D 部署和运动问题。