You are in:Home/Publications/AI-Enabled UAV Communications: Challenges and Future Directions

Dr. Rokaia Mounir Zaki Emam :: Publications:

Title:
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:
Full paper Rokaia Mounir Zaki Emam_AI-Enabled_UAV_Communications_Challenges_and_Future_Directions.pdf
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.

Google ScholarAcdemia.eduResearch GateLinkedinFacebookTwitterGoogle PlusYoutubeWordpressInstagramMendeleyZoteroEvernoteORCIDScopus