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Multi-Agent Reinforcement Learning for Adaptive User Association in Dynamic mmWave Networks.

, , , , and . IEEE Trans. Wirel. Commun., 19 (10): 6520-6534 (2020)

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Sensing of Side Lobes Interference for Blockage Prediction in Dense mmWave Networks., , and . PIMRC, page 1-6. IEEE, (2023)Pragmatic Goal-Oriented Communications Under Semantic-Effectiveness Channel Errors., , and . CCNC, page 683-689. IEEE, (2024)Coverage Extension as a Service for Flexible 6G Networks Infrastructure., , , , , , , , , and 3 other author(s). GLOBECOM (Workshops), page 1329-1334. IEEE, (2022)Transferable and Distributed User Association Policies for 5G and Beyond Networks., , and . PIMRC, page 966-971. IEEE, (2021)Learning Semantics: An Opportunity for Effective 6G Communications., and . CoRR, (2021)Energy Efficient Edge Computing: When Lyapunov Meets Distributed Reinforcement Learning., , , and . ICC Workshops, page 1-6. IEEE, (2021)Multi-Agent Deep Reinforcement Learning For Distributed Handover Management In Dense MmWave Networks., , , and . ICASSP, page 8976-8980. IEEE, (2020)Federated Multi-Agent Deep Reinforcement Learning for Dynamic and Flexible 3D Operation of 5G Multi-MAP Networks., , and . PIMRC, page 1-6. IEEE, (2023)Distributed Learning for 5G and Beyond Network Management and Orchestration. (Apprentissage distribué pour la gestion et l'orchestration des réseaux 5G et au-delà).. Grenoble Alpes University, France, (2021)Cost-Efficient and QoS-Aware User Association and 3D Placement of 6G Aerial Mobile Access Points., , and . EuCNC, page 357-362. IEEE, (2022)