Výsledky vyhledávání - "Maman, Mickael"

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

Autor: Catté, Esteban, Sana, Mohamed, Maman, Mickael

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 Int

Externí odkaz: http://arxiv.org/abs/2307.06842

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

Autor: Catté, Esteban, Sana, Mohamed, Maman, Mickael

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 t

Externí odkaz: http://arxiv.org/abs/2303.05233

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

Autor: Catté, Esteban, Sana, Mohamed, Maman, Mickael

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

Externí odkaz: http://arxiv.org/abs/2205.01390

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Towards URLLC with Proactive HARQ Adaptation

Autor: Dinh, Lam Ngoc, Labriji, Ibtissam, Maman, Mickael, Strinati, Emilio Calvanese

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 control framework dynamically selects the number of proactive retrans

Externí odkaz: http://arxiv.org/abs/2205.01644

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