Paper Info
Title
A tutorial on AI-powered 3D deployment of drone base stations: State of the art, applications and challenges
Abstract
Deploying uncrewed aerial vehicles (UAVs) as aerial base stations (BSs) to assist terrestrial connectivity has drawn significant attention in recent years. Alongside other UAV types, drones can be rapidly deployed in the air to bring Internet access to a region when serving users via terrestrial BSs is not feasible. Despite the numerous advantages of Drone-BSs, they pose several major challenges that need to be addressed in order to offer a stable drone-assisted wireless network service. Optimal placement of Drone-BSs dynamically in the air and moving them to ensure maximum coverage is among the most critical challenges. In this article, we present a comprehensive tutorial on 3D location optimization of Drone-BSs. We first introduce UAV-assisted wireless networks along with their use cases and associated challenges. We move towards 3D location optimization of Drone-BSs, as one of the most important challenges, and present a detailed review of the state-of-the-art techniques in the literature that can tackle this challenge. Starting with the conventional solutions to 3D placement of Drone-BSs, we review the existing artificial intelligence (AI)-based solutions to address this problem following upon the presentation of the AI techniques that are used by the existing studies on Drone-BSs placement.
Year
DOI
Venue
2022
10.1016/j.vehcom.2022.100474
Vehicular Communications
Keywords
DocType
Volume
Uncrewed Aerial Vehicles (UAVs),Aerial Base Stations,5G,Artificial intelligence,Deep neural networks
Journal
36
ISSN
Citations 
PageRank 
2214-2096
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Nahid Parvaresh100.34
Michel Kulhandjian200.34
Hovannes Kulhandjian300.34
Claude D'Amours44412.78
Burak Kantarci500.34