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

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DOI	Resolve DOI: https://doi.org/10.1109/OJVT.2024.3434486
Author	Search for: Arani, Atefeh Hajijamali; Search for: Hu, Peng ORCID identifier: https://orcid.org/0000-0002-9069-0484; Search for: Zhu, Yeying ORCID identifier: https://orcid.org/0000-0002-9019-9716
Format	Text, Article
Subject	deployment; 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
Abstract	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.
Publication date	2024-07-26
Publisher	Institute of Electrical and Electronics Engineers
Licence	Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) https://creativecommons.org/licenses/by-nc-nd/4.0/
In	IEEE Open Journal of Vehicular Technology 5 (26 July 2024): 1004–1023.
Language	English
Peer reviewed	Yes
NRC publication	This is a non-NRC publication "Non-NRC publications" are publications authored by NRC employees prior to their employment by NRC.
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Record identifier	63044015-e457-486e-88fe-499b14c0dbb0
Record created	2024-08-21
Record modified	2024-08-22

Date modified:: 2025-12-12