4 papers
Federated Multi-Agent Deep Reinforcement Learning for Dynamic and Flexible 3D Operation of 5G Multi-MAP Networks
Esteban Catté, Mohamed Sana, Mickael Maman
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 architec…
Dual-Attention Deep Reinforcement Learning for Multi-MAP 3D Trajectory Optimization in Dynamic 5G Networks
Esteban Catté, Mohamed Sana, Mickael Maman
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 traff…
Cost-Efficient and QoS-Aware User Association and 3D Placement of 6G Aerial Mobile Access Points
Esteban Catté, Mohamed Sana, Mickael Maman
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 f…
Towards URLLC with Proactive HARQ Adaptation
Lam Ngoc Dinh, Ibtissam Labriji, Mickael Maman +1
In this work, we propose a dynamic decision maker algorithm to improve the proactive HARQ protocol for beyond 5G networks. Based on Lyapunov stochastic optimization, our adaptation…