| Téléchargement | - Voir la version finale : HAPS-UAV-enabled heterogeneous networks: a deep reinforcement learning approach (PDF, 5.0 Mio)
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| DOI | Trouver le DOI : https://doi.org/10.1109/OJCOMS.2023.3296378 |
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| Auteur | Rechercher : Arani, Atefeh HajijamaliIdentifiant ORCID : https://orcid.org/0000-0002-4177-3558; Rechercher : Hu, Peng1Identifiant ORCID : https://orcid.org/0000-0002-9069-0484; Rechercher : Zhu, YeyingIdentifiant ORCID : https://orcid.org/0000-0002-9019-9716 |
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| Affiliation | - Conseil national de recherches Canada. Technologies numériques
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| Bailleur de fonds | Rechercher : High-Throughput and Secure Networks Challenge Program of National Research Council Canada; Rechercher : Natural Sciences and Engineering Research Council of Canada |
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| Format | Texte, Article |
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| Sujet | platform station; resource allocation; fairness; unmanned aerial vehicles; non-terrestrial networks; autonomous aerial vehicles; trajectory; heuristic algorithms; resource management; quality of service; uplink; deep learning |
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| Résumé | The integrated use of non-terrestrial network (NTN) entities such as the high-altitude platform station (HAPS) and low-altitude platform station (LAPS) has become essential elements in the space-air-ground integrated networks (SAGINs). However, the complexity, mobility, and heterogeneity of NTN entities and resources present various challenges from system design to deployment. This paper proposes a novel approach to designing a heterogeneous network consisting of HAPSs and unmanned aerial vehicles (UAVs) being LAPS entities. Our approach involves jointly optimizing the three-dimensional trajectory and channel allocation for aerial base stations, with a focus on ensuring fairness and the provision of quality of service (QoS) to ground users. Furthermore, we consider the load on base stations and incorporate this information into the optimization problem. The proposed approach utilizes a combination of deep reinforcement learning and fixed-point iteration techniques to determine the UAV locations and channel allocation strategies. Simulation results reveal that our proposed deep learning-based approach significantly outperforms learning-based and conventional benchmark models. |
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| Date de publication | 2023-07-18 |
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| Maison d’édition | Institute of Electrical and Electronics Engineers |
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| Licence | |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 30e08f18-c7f7-4205-8caf-2e704627d0e6 |
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| Enregistrement créé | 2023-08-23 |
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| Enregistrement modifié | 2023-08-24 |
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