Efficient Deployment of Multiple Unmanned Aerial Vehicles for Optimal Wireless Coverage

By Mohammad Mozaffari, Walid Saad, Medhi Bennis, Merouane Debbah. IEEE Communications Letters, 2016

977 citations recorded by the source corpus.

In this letter, the efficient deployment of multiple unmanned aerial vehicles (UAVs) acting as wireless base stations that provide coverage for ground users is analyzed. First, the downlink coverage probability for UAVs as a function of the altitude and the antenna gain is derived. Next, using circle packing theory, the 3-D locations of the UAVs is determined in a way that the total coverage area is maximized while maximizing the coverage lifetime of the UAVs. Our results show that, in order to mitigate interference, the altitude of the UAVs must be properly adjusted based on the beamwidth of the directional antenna as well as coverage requirements. Furthermore, the minimum number of UAVs needed to guarantee a target coverage probability for a given geographical area is determined. Numerical results evaluate various tradeoffs.

Subfields: Aerospace Engineering, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/adf581691aef09a0da06dae8bc87248f33ca348a

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Citations in the corpus

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey (2018, background): “Whenever mobile devices, for example, cell phones, cannot establish a direct connection to the macrocell base station, drones may also serve as intermediaries, acting as flying base stations [5, 91, 178].”
Downlink Coverage Analysis for a Finite 3-D Wireless Network of Unmanned Aerial Vehicles (2017, background): “The problem of efficient placement of UAVs with slightly different objectives was studied in [13] and [14].”
Trajectory Design and Power Control for Multi-UAV Assisted Wireless Networks: A Machine Learning Approach (2018, methodology): “The authors of [31] used sphere packing theory for determining the most appropriate 3D position of the UAVs while jointly maximizing both the total coverage area and the battery operating period of the UAVs.”
Massive Autonomous UAV Path Planning: A Neural Network Based Mean-Field Game Theoretic Approach (2019, methodology): “The problem of UAV placement for supporting communication systems has been studied in [7].”
UAV Trajectory Optimization for Data Offloading at the Edge of Multiple Cells (2018, background): “maximized the downlink coverage significantly by optimizing 3D deployment of UAVs with directional antennas in [10].”
Beyond 5G With UAVs: Foundations of a 3D Wireless Cellular Network (2018, background): “However, none of the previous works on UAV communications [3]–[7], [12]–[20], [23]–[26], studied a 3D wireless network of co-existing aerial base stations and users (i.”
Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges (2018, neutral): “The authors in [228] study the optimal deployment of UAVs equipped with directional antennas, using circle packing theory.”
Communications and Control for Wireless Drone-Based Antenna Array (2017, background): “In wireless networks, drones can be used as flying base stations to provide reliable and cost-effective wireless connectivity [2]–[9].”
Multi-Agent Reinforcement Learning-Based Resource Allocation for UAV Networks (2018, positive): “In [10], a three-dimensional (3D) deployment algorithm based on circle packing is developed for maximizing the downlink coverage performance.”
Multi-Tier Drone Architecture for 5G/B5G Cellular Networks: Challenges, Trends, and Prospects (2017, methodology): “Another interesting study is [15] where the authors consider a network of multiple UAVs with directional antennas.”
Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond (2019, methodology): “The above work was then extended to [60], where by using directional UAV antenna model similar as (17), the 3D locations of a given number of UAV BSs were”
UAV assistance paradigm: State-of-the-art in applications and challenges (2020, background): “The altitude can be also determined based on the number of existing UAVs with the aim to maximize the coverage of ground networks or to achieve a given task [97].”
Wireless Communication Using Unmanned Aerial Vehicles (UAVs): Optimal Transport Theory for Hover Time Optimization (2017, background): “In particular, UAVs can be deployed as ﬂying base stations for coverage expansion and capacity enhancement of terrestrial cellular networks [2]–[10].”