3-D Placement of an Unmanned Aerial Vehicle Base Station (UAV-BS) for Energy-Efficient Maximal Coverage

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

852 citations recorded by the source corpus.

Unmanned aerial vehicle mounted base stations (UAV-BSs) can provide wireless services in a variety of scenarios. In this letter, we propose an optimal placement algorithm for UAV-BSs that maximizes the number of covered users using the minimum transmit power. We decouple the UAV-BS deployment problem in the vertical and horizontal dimensions without any loss of optimality. Furthermore, we model the UAV-BS deployment in the horizontal dimension as a circle placement problem and a smallest enclosing circle problem. Simulations are conducted to evaluate the performance of the proposed method for different spatial distributions of the users.

Subfields: Aerospace Engineering, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/acd20371004c8a6911509a8e7aa7e6f854f27e3b

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Citations in the corpus

Massive UAV-to-Ground Communication and its Stable Movement Control: A Mean-Field Approach (2018, background): “Most of the current UAV literature is focused on solving air-to-ground wireless communication problems in sparse deployment scenarios [5], [6], [8], [9].”
Multi-Agent Reinforcement Learning-Based Resource Allocation for UAV Networks (2018, methodology): “For the probabilistic model, the channel parameters in the simulations follow [7], where a = 9.”
Distributed Drone Base Station Positioning for Emergency Cellular Networks Using Reinforcement Learning (2018, background): “…works covering the deployment of drones in emergency situations [1, 8], other works covering the deployment of drones with backhaul limitations [11, 13, 15], and others considering the positioning of aerial platforms [3, 5–7, 9–11, 14], only [3] proposes an intelligent solution in order to…”
Energy Efficient Resource Allocation in UAV-Enabled Mobile Edge Computing Networks (2019, background): “To maximize the number of covered users using the minimum transmit power, an optimal location and altitude placement algorithm was investigated in [17] for UAV-base stations (BSs).”
Cellular-Enabled UAV Communication: A Connectivity-Constrained Trajectory Optimization Perspective (2018, background): “The work in [35] was extended in [36]–[38], with additional considerations of the backhaul rate and bandwidth constraint for the UAV [36], transmit power consumption [37], or heterogeneous QoS constraints for the users [38].”
A Survey of Channel Modeling for UAV Communications (2018, methodology): “The path loss model in [10] addressed the technical challenges in UAV communication, such as optimum deployment of the UAV in [11] and [12], outage and bit–error rate (BER) analysis in [84], energy efficiency of UAV networks in [85]–[87], interference management in multi–UAV scenario in [88], latenc”
IRS-Assisted Secure UAV Transmission via Joint Trajectory and Beamforming Design (2022, methodology): “proposed an efficient placement method for UAV-BSs to maximize the number of covered users with the minimum transmit power in [8].”
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Multi-Agent Deep Reinforcement Learning-Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing (2020, background): “[16] investigated coverage-placement problem of UAV-BSs and proposed an optimal placement algorithm for maximizing the number of users supported.”
3-D Placement of an Unmanned Aerial Vehicle Base Station for Maximum Coverage of Users With Different QoS Requirements (2017, background): “It was shown in [6] that for any QoS requirement Lk th, the optimal elevation angle θ ∗, that maximizes the coverage radius, is constant and depends only on the environment.”
Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges (2018, neutral): “In [223], the authors propose an optimal placement algorithm for UAV that maximizes the number of covered users using”
Trajectory Design and Power Control for Multi-UAV Assisted Wireless Networks: A Machine Learning Approach (2018, background): “Early research contributions have studied the deployment of a single UAV either to provide maximum radio coverage on the ground [23, 24] or to maximize the number of users by using the minimum transmit power [25].”
Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach (2020, background): “, as flying base stations [32], with mobile cloudlets for computation offloading [33], and for search and rescue missions [34].”
Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both? (2019, background): “through slicing and reprogrammability, the physical network infrastructure will also be made flexible by using flying base stations implemented through drones and unmanned aerial vehicles that can be redeployed based on heterogeneous traffic conditions to support on-demand connectivity requests [10]”
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].”