This paper addresses the efficient management of Mobile Access Points (MAPs),
which are Unmanned Aerial Vehicles (UAV), in 5G networks. We propose a
two-level hierarchical architecture, which dynamically reconfigures the network
while considering Integrated Access-Backhaul (IAB) constraints. The high-layer
decision process determines the number of MAPs through consensus, and we
develop a joint optimization process to account for co-dependence in network
self-management. In the low-layer, MAPs manage their placement using a
double-attention based Deep Reinforcement Learning (DRL) model that encourages
cooperation without retraining. To improve generalization and reduce
complexity, we propose a federated mechanism for training and sharing one
placement model for every MAP in the low-layer. Additionally, we jointly
optimize the placement and backhaul connectivity of MAPs using a
multi-objective reward function, considering the impact of varying MAP
placement on wireless backhaul connectivity.

本文提出了一种基于两级层次结构的管理方法，结合统一接入 - 回传约束、联邦式机器学习和双注意力深度强化学习模型，在考虑移动接入点规划的同时，优化无人机的部署和网络结构设计，提高 5G 网络中移动接入点的效率。