On the Number and 3D Placement of Drone Base Stations in Wireless Cellular Networks

By Elham Kalantari, Halim Yanikomeroglu, Abbas Yongaçoglu. IEEE Vehicular Technology Conference, 2016

388 citations recorded by the source corpus.

Using drone base stations (drone-BSs) in wireless networks has started attracting attention. Drone-BSs can assist the ground BSs in both capacity and coverage enhancement. One of the important problems about integrating drone-BSs to cellular networks is the management of their placement to satisfy the dynamic system requirements. In this paper, we propose a method to find the positions of drone-BSs in an area with different user densities using a heuristic algorithm. The goal is to find the minimum number of drone-BSs and their 3D placement so that all the users are served. Our simulation results show that the proposed approach can satisfy the quality-of-service requirements of the network.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/517bd6c03db5c8c4a1708ab47044103e2c6435ab

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Citations in the corpus

3-D Placement of an Unmanned Aerial Vehicle Base Station (UAV-BS) for Energy-Efficient Maximal Coverage (2017, methodology): “[8] developed a heuristic algorithm based on particle swarm optimization.”
A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems (2018, methodology): “[112] jointly optimized the locations and num-”
UAV assistance paradigm: State-of-the-art in applications and challenges (2020, background): “The trajectory of UAVs has to be thoroughly studied in order to satisfy their mission requirements and at the same time fulﬁlling the optimum throughput for the ground terrestrial networks and GUs [196].”
Wireless Communication Using Unmanned Aerial Vehicles (UAVs): Optimal Transport Theory for Hover Time Optimization (2017, background): “In such case, one must consider the randomness associated with the LoS and NLoS links [8], [6].”
Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond (2019, methodology): “Another sensible design objective for UAV placement is to minimize the number of required UAVs while satisfying the communication requirement of ground users [112,113].”
The New Frontier in RAN Heterogeneity: Multi-Tier Drone-Cells (2016, background): “Therefore, it is of critical importance to determine the parameters affecting a drone-cell’s performance, such as its altitude, location, and trajectory based on the network demands [1], [11].”
Beyond 5G With UAVs: Foundations of a 3D Wireless Cellular Network (2018, background): “For instance, in [7], the authors proposed an algorithm for jointly optimizing the locations and number of drones to maximize wireless coverage.”
Cellular-Enabled UAV Communication: A Connectivity-Constrained Trajectory Optimization Perspective (2018, methodology): “The same problem was also considered in [40] under the probabilistic LoS channel model, where a heuristic algorithm based on particle swarm optimization was proposed.”
Internet of vehicles in big data era (2018, neutral): “Some heuristic algorithms are also proposed to find the optimal deployment of drones to assist future RAN in 5G era [69]−[71].”
Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges (2018, neutral): “The authors in [234] propose a method to find the placements of UAVs in an area with different user densities using the particle swarm optimization.”
Distributed Drone Base Station Positioning for Emergency Cellular Networks Using Reinforcement Learning (2018, methodology): “As the authors show in [3], the approach is tested in two fixed scenarios, without considering user mobility.”
Multi-Tier Drone Architecture for 5G/B5G Cellular Networks: Challenges, Trends, and Prospects (2017, background): “Another interesting study is [11] where a 3-”
3-D Placement of an Unmanned Aerial Vehicle Base Station for Maximum Coverage of Users With Different QoS Requirements (2017, methodology): “[10] developed a particle swarm optimization framework to find the minimum number of UAV-BSs and their locations to serve a particular region.”