Abstract
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 mm-wave 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 multi-agent 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.
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