3-D Placement of an Unmanned Aerial Vehicle Base Station for Maximum Coverage of Users With Different QoS Requirements

By Mohamed Alzenad, Amr Fawzy El-Keyi, Halim Yanikomeroglu. IEEE Wireless Communications Letters, 2017

448 citations recorded by the source corpus.

The need for a rapid-to-deploy solution for providing wireless cellular services can be realized by unmanned aerial vehicle base stations (UAV-BSs). To the best of our knowledge, this letter is the first in literature that studies a novel 3-D UAV-BS placement that maximizes the number of covered users with different quality-of-service requirements. We model the placement problem as a multiple circles placement problem and propose an optimal placement algorithm that utilizes an exhaustive search (ES) over a 1-D parameter in a closed region. We also propose a low-complexity algorithm, namely, maximal weighted area (MWA) algorithm to tackle the placement problem. Numerical simulations are presented showing that the MWA algorithm performs very close to the ES algorithm with a significant complexity reduction.

Subfields: Aerospace Engineering, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/6042604ddb072614ee2b71cc95db2ffc868eae4f

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Citations in the corpus

Beyond 5G With UAVs: Foundations of a 3D Wireless Cellular Network (2018, background): “The work in [13] studied the optimal 3D deployment of UAVs for maximizing the number of covered ground users”
Routing in Flying Ad Hoc Networks: A Comprehensive Survey (2020, background): “This work is further extended in [40] to maximize the coverage of users with different QoS requirements, where the problem is modeled as a multiple-circle placement problem and a heuristic algorithm, called maximal weighted area, is proposed to solve the problem.”
Energy Efficient Resource Allocation in UAV-Enabled Mobile Edge Computing Networks (2019, background): “With different quality-of-service (QoS) requirements of users, authors in [18] studied the three-dimension (3D) UAV-BS placement that maximizes the number of covered users.”
A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems (2018, background): “As a result, optimal deployment of UAVs has received significant attention [7], [8], [11]–[13], [34], [35], [37], [112], [118], [119].”
Unmanned Aerial Vehicles for Search and Rescue: A Survey (2023, background): “The paper [31] studied the three-dimensional deployment of UAV base stations (UAVBS) for maximum user coverage with different quality of service, given the restricted transmitting power and quality requirement for services.”
Cellular UAV-to-X Communications: Design and Optimization for Multi-UAV Networks (2018, background): “In [17], the authors studied a 3-D UAV-BS placement to maximize the number of covered users with different quality-of-service requirements.”
UAV Trajectory Planning for Data Collection from Time-Constrained IoT Devices (2020, background): “Other works have focused on the optimal deployment/placement of UAVs to maximize the coverage probability and capacity [12].”
Cellular-Enabled UAV Communication: A Connectivity-Constrained Trajectory Optimization Perspective (2018, background): “for the UAV [39], transmit power consumption [40], or heterogeneous QoS constraints for the users [41].”
Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges (2018, unknown): “The authors in [230] study an efficient 3D UAV placement that maximizes the number of covered users with different Quality-of-Service requirements.”
Communications and Control for Wireless Drone-Based Antenna Array (2017, background): “For instance, in [3], the authors studied the optimal 3D placement of UAVs for maximizing the number of covered users with different quality-of-service (QoS) requirements.”
3D Trajectory Optimization in Rician Fading for UAV-Enabled Data Harvesting (2019, unknown): “, [23]–[28] and the references therein).”
IRS-Assisted Secure UAV Transmission via Joint Trajectory and Beamforming Design (2022, background): “Furthermore, they investigated a novel 3D UAV-BS placement to maximize the number of covered users with different quality of service requirements in [9].”
Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond (2019, background): “2) Perfect ULI: On the other hand, when the ULI or even the instantaneous CSI of the served GTs is known, the UAV placement can be designed for various objectives, such as maximizing the number of covered users [110]–[112], maximizing the communication throughput [63], [113], [114], or minimizing th”
Energy-Efficient UAV Control for Effective and Fair Communication Coverage: A Deep Reinforcement Learning Approach (2018, background): “[29] studied a 3D UAV-BS placement problem that maximizes the number of covered users with different Quality-of-Service (QoS) requirements.”