| publication name | AI-Enabled UAV Communications: Challenges and Future Directions |
|---|---|
| Authors | AMIRA O. HASHESH, SHERIEF HASHIMA , ROKAIA M. ZAKI, MOSTAFA M. FOUDA , KOHEI HATANO AND ADLY S. TAG ELDIEN |
| year | 2022 |
| keywords | Unmanned aerial vehicles (UAVs), artificial intelligence (AI), deep learning (DL), metalearning, federated learning (FL), reinforcement learning (RL) |
| journal | IEEE Access |
| volume | 10 |
| issue | Not Available |
| pages | 92048-92066 |
| publisher | IEEE |
| Local/International | International |
| Paper Link | https://ieeexplore.ieee.org/document/9869817 |
| Full paper | download |
| Supplementary materials | Not Available |
Abstract
Recently, unmanned aerial vehicles (UAVs) communications gained significant concentration as a talented technology for future wireless communications using its remarkable advantages and broad applicability. Furthermore, UAV networks’ high complex configurations and designs encourage researchers to leverage relevant artificial intelligence (AI) techniques for better beyond fifth-generation (B5G)/sixthgeneration (6G) services. This article summarizes AI-aided UAV solutions designated for forthcoming wireless networks. Besides, we deliver a comprehensive summary of machine learning (ML) approaches, including their applications and valuable contributions towards effective UAV network implementations, particularly advanced ML ones like bandits, federated learning (FL), meta-learning, etc. Finally, detailed UAV communication-related future research scopes and challenges is highlighted.