Efficient 3-D placement of an aerial base station in next generation cellular networks

By R. Irem, Bor Yaliniz, Amr Fawzy El-Keyi, Halim Yanikomeroglu. 2016 IEEE International Conference on Communications (ICC), 2016

741 citations recorded by the source corpus.

Agility and resilience requirements of future cellular networks may not be fully satisfied by terrestrial base stations in cases of unexpected or temporary events. A promising solution is assisting the cellular network via low-altitude unmanned aerial vehicles equipped with base stations, i.e., drone-cells. Although drone-cells provide a quick deployment opportunity as aerial base stations, efficient placement becomes one of the key issues. In addition to mobility of the drone-cells in the vertical dimension as well as the horizontal dimension, the differences between the air-to-ground and terrestrial channels cause the placement of the drone-cells to diverge from placement of terrestrial base stations. In this paper, we first highlight the properties of the drone-cell placement problem, and formulate it as a 3-D placement problem with the objective of maximizing the revenue of the network. After some mathematical manipulations, we formulate an equivalent quadratically-constrained mixed integer non-linear optimization problem and propose a computationally efficient numerical solution for this problem. We verify our analytical derivations with numerical simulations and enrich them with discussions which could serve as guidelines for researchers, mobile network operators, and policy makers.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/1284251c65b470028729aa76d0d6237c7a65139d

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Citations in the corpus

3-D Placement of an Unmanned Aerial Vehicle Base Station for Maximum Coverage of Users With Different QoS Requirements (2017, methodology): “The work in [8] made a further step and deployed the UAV-BS based on the locations of the users and formulated the UAVBS placement problem as a quadratically-constraint mixed integer non-linear problem.”
Trajectory Design for Completion Time Minimization in UAV-Enabled Multicasting (2018, background): “Along this direction, significant research efforts have been devoted to optimizing the UAV placement in two dimensional (2D) or 3D space [8]–[17], by exploiting the unique channel characteristics of the UAV-ground links.”
The New Frontier in RAN Heterogeneity: Multi-Tier Drone-Cells (2016, methodology): “A drone-BS is a low-altitude2 unmanned aerial vehicle equipped with transceivers to assist the cellular network [1], and drone-cell is the corresponding coverage area.”
Mobile Unmanned Aerial Vehicles (UAVs) for Energy-Efficient Internet of Things Communications (2017, methodology): “On the other hand, UAVs can act as mobile aerial base stations to provide reliable downlink and uplink communications for ground users and boost the capacity of wireless networks [1], [3], [6]–[10].”
Survey on UAV Cellular Communications: Practical Aspects, Standardization Advancements, Regulation, and Security Challenges (2018, background): “[79] attempted 3D placement optimization for aerial BSs in the context of heterogeneous networks (aerial BSs are used to augment terrestrial BSs), but observed that there are quadratic, exponential, and binary terms in the problem formulation, which makes it a mixed integer non-linear problem.”
A Survey of Channel Modeling for UAV Communications (2018, background): “It increases the number of users in cellular–assisted UAV network in [92] and UAV for data caching purpose in [93].”
Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond (2019, methodology): “[115] Two UAVs 3D location Given a target rectangular area, maximize the fraction of coverage area using two UAV BSs Maximum separation of the two UAV BSs subject to coverage area constraint [107]–[109] Single UAV 3D location Given user locations, maximize the number of served users Mixed-integer no”
Offline and Online Search: UAV Multiobjective Path Planning Under Dynamic Urban Environment (2018, background): “S INCE the federal aviation administration unveiled plans for domestic unmanned aerial vehicles (UAVs) to take off by 2015 [1], the usage of UAVs has received significant attention [2]–[4].”
UAV Communications for 5G and Beyond: Recent Advances and Future Trends (2019, neutral): “to characterize the UAV-to-ground channels [36]–[38].”
Placement Optimization of UAV-Mounted Mobile Base Stations (2016, methodology): “The authors in [3] adopt a probabilistic LOS channel model and study the 3-dimensional (3D) placement of a single aerial BS to ofﬂoad as many GTs as possible from the ground BS.”
3-D Placement of an Unmanned Aerial Vehicle Base Station (UAV-BS) for Energy-Efficient Maximal Coverage (2017, methodology): “[7] assumed the UAV-BS transmits at full power and formulated the 3D UAV-BS placement problem as a quadratically-constrained mixed integer non-linear problem.”
On the Number and 3D Placement of Drone Base Stations in Wireless Cellular Networks (2016, methodology): “In [5] the authors propose using a drone-BS to maximize the revenue of the network.”
Optimal 3D-Trajectory Design and Resource Allocation for Solar-Powered UAV Communication Systems (2018, background): “The placement of UAVs in the threedimensional (3D) space for maximization of the number of served users and the coverage area was studied in [8] and [9], respectively.”