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.

翻译：本文研究了5G网络中移动接入点（MAP，即无人机）的高效管理问题。我们提出了一种双层分层架构，该架构在考虑接入回传一体化（IAB）约束的同时动态重构网络。高层决策过程通过共识机制确定MAP数量，并开发联合优化流程以解决网络自管理中的相互依赖问题。在低层，MAP采用基于双重注意力的深度强化学习（DRL）模型进行部署管理，该模型无需重新训练即可促进协作。为提升泛化能力并降低复杂度，我们提出了一种联邦机制，用于在低层为每个MAP训练并共享统一的部署模型。此外，我们采用多目标奖励函数联合优化MAP的部署位置与回传连接性，同时考虑不同MAP部署对无线回传连接性的影响。