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UAV-assisted space-air-ground integrated networks: a technical review of recent learning algorithms

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DOITrouver le DOI : https://doi.org/10.1109/OJVT.2024.3434486
AuteurRechercher : ; Rechercher : Identifiant ORCID : https://orcid.org/0000-0002-9069-0484; Rechercher : Identifiant ORCID : https://orcid.org/0000-0002-9019-9716
FormatTexte, Article
Sujetdeployment; heuristic algorithms; reinforcement learning; satellite networks; terrestrial networks; unmanned aerial vehicles (UAVs); satellites; autonomous aerial vehicles; throughput; three-dimensional displays; space-air-ground integrated networks; heuristic algorithms; optimization
Résumé
Recent technological advancements in space, air, and ground components have made possible a new network paradigm called “space-air-ground integrated network” (SAGIN). Unmanned aerial vehicles (UAVs) play a key role in SAGINs. However, due to UAVs' high dynamics and complexity, real-world deployment of a SAGIN becomes a significant barrier to realizing such SAGINs. UAVs are expected to meet key performance requirements with limited maneuverability and resources with space and terrestrial components. Therefore, employing UAVs in various usage scenarios requires well-designed planning in algorithmic approaches. This paper provides an essential review and analysis of recent learning algorithms in a UAV-assisted SAGIN. We consider possible reward functions and discuss the state-of-the-art algorithms for optimizing the reward functions, including Q-learning, deep Q-learning, multi-armed bandit, particle swarm optimization, and satisfaction-based learning algorithms. Unlike other survey papers, we focus on the methodological perspective of the optimization problem, applicable to various missions on a SAGIN. We consider real-world configurations and the 2-dimensional (2D) and 3-dimensional (3D) UAV trajectories to reflect deployment cases. Our simulations suggest the 3D satisfaction-based learning algorithm outperforms other approaches in most cases. With open challenges discussed at the end, we aim to provide design and deployment guidelines for UAV-assisted SAGINs.
Date de publication
Maison d’éditionInstitute of Electrical and Electronics Engineers
Licence
Dans
  • IEEE Open Journal of Vehicular Technology 5 (26 juillet 2024) : 1004–1023.
Langueanglais
Publications évaluées par des pairsOui
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Identificateur de l’enregistrement63044015-e457-486e-88fe-499b14c0dbb0
Enregistrement créé2024-08-21
Enregistrement modifié2024-08-22

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