Placement Optimization of UAV-Mounted Mobile Base Stations

By Jiangbin Lyu, Yong Zeng, Rui Zhang, Teng Joon Lim. IEEE Communications Letters, 2016

865 citations recorded by the source corpus.

In terrestrial communication networks without fixed infrastructure, unmanned aerial vehicle-mounted mobile base stations (MBSs) provide an efficient solution to achieve wireless connectivity. This letter aims to minimize the number of MBSs needed to provide wireless coverage for a group of distributed ground terminals (GTs), ensuring that each GT is within the communication range of at least one MBS. We propose a polynomial-time algorithm with successive MBS placement, where the MBSs are placed sequentially starting on the area perimeter of the uncovered GTs along a spiral path toward the center, until all GTs are covered. Numerical results show that the proposed algorithm performs favorably compared with other schemes in terms of the number of required MBSs as well as time complexity.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/d124d7648e4525e0751eed14e557fccfadd39ad0

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Citations in the corpus

Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges (2018, neutral): “The authors in [238] aim to minimize the number of UAVs required to provide wireless coverage for”
Communications and Control for Wireless Drone-Based Antenna Array (2017, neutral): “In [14], a polynomial-time algorithm for the optimal placement of drones that provide coverage for ground terminals is proposed.”
Multi-Agent Reinforcement Learning-Based Resource Allocation for UAV Networks (2018, methodology): “By fixing the altitudes, a successive UAV placement approach was proposed to minimize the number of UAVs required while guaranteeing each ground user to be covered by at least one UAV in [12].”
Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks (2017, background): “In contrast, by fixing the altitude, the horizontal positions of UAVs are optimized in [13] to minimize the number of required UAV BSs to cover a given set of ground users.”
Mobile Unmanned Aerial Vehicles (UAVs) for Energy-Efficient Internet of Things Communications (2017, background): “The work in [12] proposed a low-complexity algorithm for the optimal deployment”
Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey (2018, background): “[160] H, Exp several drones limited communication range general [177] P, Exp drone, several drones communication constraints (eg, path loss depends on the distance between the drone and the receiver, altitude-dependent probability to have a line-of-sight connection, cases of no interference and full”
Throughput Maximization for UAV-Enabled Wireless Powered Communication Networks (2018, background): “For example, UAVs can be utilized as mobile relays to help information exchange between far-apart ground users [19], or as mobile base stations (BSs) to help enhance the wireless coverage and/or the network capacity for ground mobile users [20]–[28].”
3-D Placement of an Unmanned Aerial Vehicle Base Station for Maximum Coverage of Users With Different QoS Requirements (2017, background): “However, the work in [5]–[11] assumes that all the users have the same QoS requirement.”
Energy-Efficient UAV Communication With Trajectory Optimization (2016, background): “The first one is UAV-aided ubiquitous coverage [2]–[5], where UAVs are employed to assist the existing terrestrial communication infrastructure, if any, in providing seamless wireless coverage within the serving area.”
Energy Efficient Resource Allocation in UAV-Enabled Mobile Edge Computing Networks (2019, background): “Considering the adjustable UAVs’ locations, the UAV number minimization was considered in [19].”
A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles (2018, background): “future cellular communication networks either as a base station [148], [149] or a user equipment node [83].”
UAV Trajectory Optimization for Data Offloading at the Edge of Multiple Cells (2018, background): “in [9], to minimize the number of UAVs that can cover all the ground terminals.”
Survey on UAV Cellular Communications: Practical Aspects, Standardization Advancements, Regulation, and Security Challenges (2018, methodology): “Works in [76]–[82] considers only the access link in their optimization formulation and assumes that the backhaul link has infinite bandwidth.”
Wireless Communication Using Unmanned Aerial Vehicles (UAVs): Optimal Transport Theory for Hover Time Optimization (2017, methodology): “In [10], the authors proposed a polynomial-time algorithm for the optimal placement of UAVs that provide coverage for ground terminals.”
Energy Tradeoff in Ground-to-UAV Communication via Trajectory Design (2017, background): “One line of works mainly focus on optimizing the placement/deployment of static or quasistatic UAVs, to achieve the maximum communication coverage of GTs [3]–[7].”
Cellular-Enabled UAV Communication: A Connectivity-Constrained Trajectory Optimization Perspective (2018, methodology): “Moreover, the joint deployment of multiple UAVs was studied in [39], aiming to minimize the number of required UAVs while ensuring that all ground users are covered, under the LoS channel model.”
Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond (2019, background): “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].”