  6G networks require a flexible infrastructure to dynamically provide
ubiquitous network coverage. Mobile Access Points (MAP) deployment is a
promising solution. In this paper, we formulate the joint 3D MAP deployment and
user association problem over a dynamic network under interference and mobility
constraints. First, we propose an iterative algorithm to optimize the
deployment of MAPs. Our solution efficiently and quickly determines the number,
position and configuration of MAPs for highly dynamic scenarios. MAPs provide
appropriate Quality of Service (QoS) connectivity to mobile ground user in
mmwave or sub-6GHz bands and find their optimal positions in a 3D grid. Each
MAP also implies an energy cost (e.g. for travel) to be minimized. Once all
MAPs deployed, a deep multiagent reinforcement learning algorithm is proposed
to associate multiple users to multiple MAPs under interference constraint.
Each user acts as an independent agent that operates in a fully distributed
architecture and maximizes the network sum-rate.
