# UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithms

arXiv: https://arxiv.org/abs/2211.14931 · HTML: https://mlsanity.com/paper/2211.14931/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 94 references that are in the map.
- First posted on arXiv 27 Nov 2022.

## Builds on (most cited first, 19 of 19)

- [Applications of Deep Reinforcement Learning in Communications and Networking: A Survey](https://mlsanity.com/paper/1810.07862/) (2018)
- [Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial](https://mlsanity.com/paper/2011.03615/) (2020)
- [Multi-Agent Reinforcement Learning Based Resource Allocation for UAV Networks](https://mlsanity.com/paper/1810.10408/) (2018)
- [Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks](https://mlsanity.com/paper/1705.02723/) (2017)
- [Millimeter Wave Channel Modeling and Cellular Capacity Evaluation](https://mlsanity.com/paper/1312.4921/) (2013)
- [Path Planning for UAV-Mounted Mobile Edge Computing with Deep Reinforcement Learning](https://mlsanity.com/paper/2001.10268/) (2020)
- [Trajectory Design and Power Control for Multi-UAV Assisted Wireless Networks: A Machine Learning Approach](https://mlsanity.com/paper/1812.07665/) (2018)
- [Reinforcement Learning in Multiple-UAV Networks: Deployment and Movement Design](https://mlsanity.com/paper/1904.05242/) (2019)
- [Deep Reinforcement Learning for Minimizing Age-of-Information in UAV-assisted Networks](https://mlsanity.com/paper/1905.02993/) (2019)
- [3D UAV Trajectory and Data Collection Optimisation via Deep Reinforcement Learning](https://mlsanity.com/paper/2106.03129/) (2021)
- [Integrating LEO Satellites and Multi-UAV Reinforcement Learning for Hybrid FSO/RF Non-Terrestrial Networks](https://mlsanity.com/paper/2010.10138/) (2020)
- [On the Number and 3D Placement of Drone Base Stations in Wireless Cellular Networks](https://mlsanity.com/paper/1804.08415/) (2018)
- [Towards Big data processing in IoT: Path Planning and Resource Management of UAV Base Stations in Mobile-Edge Computing System](https://mlsanity.com/paper/1906.05023/) (2019)
- [5G from Space: An Overview of 3GPP Non-Terrestrial Networks](https://mlsanity.com/paper/2103.09156/) (2021)
- [A Survey of Wireless Networks for Future Aerial COMmunications (FACOM)](https://mlsanity.com/paper/2111.04175/) (2021)
- [Reinforcement Learning-Based Trajectory Design for the Aerial Base Stations](https://mlsanity.com/paper/1906.09550/) (2019)
- [Joint Access and Backhaul Resource Management in Satellite-Drone Networks: A Competitive Market Approach](https://mlsanity.com/paper/1908.11038/) (2019)
- [Context-Aware Mobility Management in HetNets: A Reinforcement Learning Approach](https://mlsanity.com/paper/1505.01625/) (2015)
- [A User-Centric Cooperative Scheme for UAV Assisted Wireless Networks in Malfunction Areas](https://mlsanity.com/paper/1902.01940/) (2019)

## Built on by (most cited first, 3 of 3)

- [Large Language Model-Assisted UAV Operations and Communications: A Multifaceted Survey and Tutorial](https://mlsanity.com/paper/2602.19534/) (2026)
- [Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approaches](https://mlsanity.com/paper/2507.14633/) (2025)
- [LAPF: LLM-Agent-Based Path Finder Using the UAVScenes Dataset](https://mlsanity.com/paper/2608.15175/) (2026)

## Abstract (arXiv)

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.