Dual-Attention Deep Reinforcement Learning for Multi-MAP 3D Trajectory Optimization in Dynamic 5G Networks

by   Esteban Catté, et al.

5G and beyond networks need to provide dynamic and efficient infrastructure management to better adapt to time-varying user behaviors (e.g., user mobility, interference, user traffic and evolution of the network topology). In this paper, we propose to manage the trajectory of Mobile Access Points (MAPs) under all these dynamic constraints with reduced complexity. We first formulate the placement problem to manage MAPs over time. Our solution addresses time-varying user traffic and user mobility through a Multi-Agent Deep Reinforcement Learning (MADRL). To achieve real-time behavior, the proposed solution learns to perform distributed assignment of MAP-user positions and schedules the MAP path among all users without centralized user's clustering feedback. Our solution exploits a dual-attention MADRL model via proximal policy optimization to dynamically move MAPs in 3D. The dual-attention takes into account information from both users and MAPs. The cooperation mechanism of our solution allows to manage different scenarios, without a priory information and without re-training, which significantly reduces complexity.


page 1

page 6


Federated Multi-Agent Deep Reinforcement Learning for Dynamic and Flexible 3D Operation of 5G Multi-MAP Networks

This paper addresses the efficient management of Mobile Access Points (M...

Cost-Efficient and QoS-Aware User Association and 3D Placement of 6G Aerial Mobile Access Points

6G networks require a flexible infrastructure to dynamically provide ubi...

Deep Reinforcement Learning for URLLC data management on top of scheduled eMBB traffic

With the advent of 5G and the research into beyond 5G (B5G) networks, a ...

TinyQMIX: Distributed Access Control for mMTC via Multi-agent Reinforcement Learning

Distributed access control is a crucial component for massive machine ty...

Dynamic Multichannel Access via Multi-agent Reinforcement Learning: Throughput and Fairness Guarantees

We consider a multichannel random access system in which each user acces...

Transferable and Distributed User Association Policies for 5G and Beyond Networks

We study the problem of user association, namely finding the optimal ass...

Reinforced Imitative Graph Learning for Mobile User Profiling

Mobile user profiling refers to the efforts of extracting users' charact...

Please sign up or login with your details

Forgot password? Click here to reset