Wireless Communication Using Unmanned Aerial Vehicles (UAVs): Optimal Transport Theory for Hover Time Optimization

By Mohammad Mozaffari, Walid Saad, Medhi Bennis, Merouane Debbah. IEEE Transactions on Wireless Communications, 2017

339 citations recorded by the source corpus.

In this paper, the effective use of flight-time constrained unmanned aerial vehicles (UAVs) as flying base stations that provide wireless service to ground users is investigated. In particular, a novel framework for optimizing the performance of such UAV-based wireless systems in terms of the average number of bits (data service) transmitted to users as well as the UAVs’ hover duration (i.e. flight time) is proposed. In the considered model, UAVs hover over a given geographical area to serve ground users that are distributed within the area based on an arbitrary spatial distribution function. In this case, two practical scenarios are considered. In the first scenario, based on the maximum possible hover times of UAVs, the average data service delivered to the users under a fair resource allocation scheme is maximized by finding the optimal cell partitions associated to the UAVs. Using the powerful mathematical framework of optimal transport theory, this cell partitioning problem is proved to be equivalent to a convex optimization problem. Subsequently, a gradient-based algorithm is proposed for optimally partitioning the geographical area based on the users’ distribution, hover times, and locations of the UAVs. In the second scenario, given the load requirements of ground users, the minimum average hover time that the UAVs need for completely servicing their ground users is derived. To this end, first, an optimal bandwidth allocation scheme for serving the users is proposed. Then, given this optimal bandwidth allocation, optimal cell partitions associated with the UAVs are derived by exploiting the optimal transport theory. Simulation results show that our proposed cell partitioning approach leads to a significantly higher fairness among the users compared with the classical weighted Voronoi diagram. Furthermore, the results demonstrate that the average hover time of the UAVs can be reduced by 64% by adopting the proposed optimal bandwidth allocation scheme as well as the optimal cell partitioning approach. In addition, our results reveal an inherent tradeoff between the hover time of UAVs and bandwidth efficiency while serving the ground users.

Subfields: Aerospace Engineering, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/ab4e45c96d9bdf2e53fc2ad8d86dd28b86b0f1ae

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

In Computer Networks and Communications

Citations in the corpus

Cellular UAV-to-X Communications: Design and Optimization for Multi-UAV Networks (2018, background): “The work [21] investigated the scenario where UAVs served as flying BSs to provide wireless service to ground users, and optimized the downlink data rate and UAV hover duration.”
UAV assistance paradigm: State-of-the-art in applications and challenges (2020, background): “Moreover, creating new hardware devices that are able of modifying the wireless network, according to the varying trafﬁc demands, shadowing, undeﬁned direction of communication, varying radiation patterns, and interference [78], [79].”
Mobile Unmanned Aerial Vehicles (UAVs) for Energy-Efficient Internet of Things Communications (2017, background): “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], [2], [6]–[12].”
Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges (2018, neutral): “In [233], the authors propose an efficient framework for optimizing the performance of UAV-based wireless systems in terms of the average number of bits transmitted to users and”
Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks (2017, background): “Moreover, due to their flight time constraints [166], UAVs can only collect data within a limited time period.”
A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems (2018, background): “ment schemes that lead to the maximum system performance in terms of energy efficiency, throughput, and delay under explicit flight time constraints of UAVs [41], [154].”
Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey (2018, background): “[183] M, P, E, Exp limited flight time, communication constraints (limited communication range, interferences, signal quality depends on distance) general”
Space-Air-Ground Integrated Network: A Survey (2018, background): “[111] investigated the hover time and load constraints in UAV communications, and considered a network in which multiple UAVs were deployed as aerial BSs to provide wireless service to ground users.”
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],[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], latency in ”
UAV Communications for 5G and Beyond: Recent Advances and Future Trends (2019, background): “, flying BSs or mobile relays) by mounting communication transceivers to provide/enhance communication services to ground targets in high traffic demand and overloaded situations, which is commonly referred to as UAV-assisted communications [5]–[9].”
Trajectory Design and Power Control for Multi-UAV Assisted Wireless Networks: A Machine Learning Approach (2018, background): “The LoS probability can be expressed as [12] where θ k n ( t ) = sin − 1 ( h n ( t ) d kn ( t ) ) is the elevation angle between the UAV and the user k n .”
Applications of unmanned aerial vehicle (UAV) in road safety, traffic and highway infrastructure management: Recent advances and challenges (2020, neutral): “Researchers have done different experiments to enhance communication to the ground user by minimizing the flight time of multiple drones (Mozaffari et al., 2017).”
Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond (2019, neutral): “[79] M.”
Beyond 5G With UAVs: Foundations of a 3D Wireless Cellular Network (2018, methodology): “We assume that each drone-BS adopts a frequency division multiple access (FDMA) technique (as done in [19] and [28]) when servicing its associated drone-UEs.”
Communications and Control for Wireless Drone-Based Antenna Array (2017, methodology): “The work in [15] minimized the hover time of drone base stations by deriving the optimal cell association schemes.”
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial (2017, background): “Moreover, due to their flight time constraints [89], UAVs can only collect data within a limited time period.”