UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithms
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 c...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Vehicular Technology |
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| Online Access: | https://ieeexplore.ieee.org/document/10612249/ |
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| author | Atefeh Hajijamali Arani Peng Hu Yeying Zhu |
| author_facet | Atefeh Hajijamali Arani Peng Hu Yeying Zhu |
| author_sort | Atefeh Hajijamali Arani |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5ecc37ed99ec4ec5b1ac1008aeff489e |
| institution | Kabale University |
| issn | 2644-1330 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Vehicular Technology |
| spelling | doaj-art-5ecc37ed99ec4ec5b1ac1008aeff489e2025-01-30T00:04:06ZengIEEEIEEE Open Journal of Vehicular Technology2644-13302024-01-0151004102310.1109/OJVT.2024.343448610612249UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning AlgorithmsAtefeh Hajijamali Arani0Peng Hu1https://orcid.org/0000-0002-9069-0484Yeying Zhu2https://orcid.org/0000-0002-9019-9716Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, CanadaDepartment of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, CanadaDepartment of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, CanadaRecent 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.https://ieeexplore.ieee.org/document/10612249/Deploymentheuristic algorithmsreinforcement learningsatellite networksterrestrial networksunmanned aerial vehicles (UAVs) |
| spellingShingle | Atefeh Hajijamali Arani Peng Hu Yeying Zhu UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithms IEEE Open Journal of Vehicular Technology Deployment heuristic algorithms reinforcement learning satellite networks terrestrial networks unmanned aerial vehicles (UAVs) |
| title | UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithms |
| title_full | UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithms |
| title_fullStr | UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithms |
| title_full_unstemmed | UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithms |
| title_short | UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithms |
| title_sort | uav assisted space air ground integrated networks a technical review of recent learning algorithms |
| topic | Deployment heuristic algorithms reinforcement learning satellite networks terrestrial networks unmanned aerial vehicles (UAVs) |
| url | https://ieeexplore.ieee.org/document/10612249/ |
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