# Artificial Intelligence Aided Next-Generation Networks Relying on UAVs

arXiv: https://arxiv.org/abs/2001.11958 · HTML: https://mlsanity.com/paper/2001.11958/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2020.

## Built on by (most cited first, 5 of 5)

- [Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey](https://mlsanity.com/paper/2211.04324/) (2022)
- [UAVs Meet Agentic AI: A Multidomain Survey of Autonomous Aerial Intelligence and Agentic UAVs](https://mlsanity.com/paper/2506.08045/) (2025)
- [Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges](https://mlsanity.com/paper/2111.07392/) (2021)
- [A Comprehensive Review of AI-enabled Unmanned Aerial Vehicle: Trends, Vision , and Challenges](https://mlsanity.com/paper/2310.16360/) (2023)
- [Integrated Communication and Control for Energy-Efficient UAV Swarms: A Multi-Agent Reinforcement Learning Approach](https://mlsanity.com/paper/2509.23905/) (2025)

## Abstract (arXiv)

Artificial intelligence (AI) assisted unmanned aerial vehicle (UAV) aided next-generation networking is proposed for dynamic environments. In the AI-enabled UAV-aided wireless networks (UAWN), multiple UAVs are employed as aerial base stations, which are capable of rapidly adapting to the dynamic environment by collecting information about the users' position and tele-traffic demands, learning from the environment and acting upon the feedback received from the users. Moreover, AI enables the interaction amongst a swarm of UAVs for cooperative optimization of the system. As a benefit of the AI framework, several challenges of conventional UAWN may be circumvented, leading to enhanced network performance, improved reliability and agile adaptivity. As a further benefit, dynamic trajectory design and resource allocation are demonstrated. Finally, potential research challenges and opportunities are discussed.