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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.

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