A method for deploying the minimal number of UAV base stations in cellular networks

In this paper, we consider the scenario of using unmanned aerial vehicles base stations ( UAV-BSs ) to serve cellular users. In particular, we focus on finding the minimum number of UAV-BSs as well as their deployment. We propose an optimization model which minimizes the number of UAV-BSs and optimize their positions such that the user equipment ( UE ) covered ratio is no less than the expectation of network suppliers, the UEs receive acceptable downlink rates, and the UAV-BSs can work in a sustainable manner. We show the NP-hardness of this problem and then propose a method to address it. The method first estimates the range of the number of UAV-BSs and then converts the original problem to one which maximizes the UE served ratio, given the number of UAV-BSs within that range. We present a maximizing algorithm to solve it with the proof of convergence. Extensive simulations based on a realistic dataset have been conducted to demonstrate the effectiveness of the proposed method.

[1]  Akram Al-Hourani,et al.  Modeling Cellular-to-UAV Path-Loss for Suburban Environments , 2018, IEEE Wireless Communications Letters.

[2]  Kanchana Thilakarathna,et al.  A deep dive into location-based communities in social discovery networks , 2017, Comput. Commun..

[3]  Jeroen Wigard,et al.  Radio Channel Modeling for UAV Communication Over Cellular Networks , 2017, IEEE Wireless Communications Letters.

[4]  Andrey V. Savkin,et al.  Deployment of Unmanned Aerial Vehicle Base Stations for Optimal Quality of Coverage , 2019, IEEE Wireless Communications Letters.

[5]  Andrey V. Savkin,et al.  A Method for Optimized Deployment of Unmanned Aerial Vehicles for Maximum Coverage and Minimum Interference in Cellular Networks , 2019, IEEE Transactions on Industrial Informatics.

[6]  Fabio Morbidi,et al.  Minimum-energy path generation for a quadrotor UAV , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[7]  Andrey V. Savkin,et al.  Proactive Deployment of Aerial Drones for Coverage over Very Uneven Terrains: A Version of the 3D Art Gallery Problem , 2019, Sensors.

[8]  Roksana Boreli,et al.  The Where and When of Finding New Friends: Analysis of a Location-based Social Discovery Network , 2013, ICWSM.

[9]  Andrey V. Savkin,et al.  Towards the Internet of Flying Robots: A Survey , 2018, Sensors.

[10]  Bernhard Rinner,et al.  Drone networks: Communications, coordination, and sensing , 2018, Ad Hoc Networks.

[11]  Andrey V. Savkin,et al.  Mobile robots in wireless sensor networks: A survey on tasks , 2019, Comput. Networks.

[12]  Andrey V. Savkin,et al.  A Method for Optimized Deployment of a Network of Surveillance Aerial Drones , 2019, IEEE Systems Journal.

[13]  Halim Yanikomeroglu,et al.  On the Number and 3D Placement of Drone Base Stations in Wireless Cellular Networks , 2016, 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall).

[14]  Andrey V. Savkin,et al.  An Algorithm of Efficient Proactive Placement of Autonomous Drones for Maximum Coverage in Cellular Networks , 2018, IEEE Wireless Communications Letters.

[15]  Halim Yanikomeroglu,et al.  Efficient 3-D placement of an aerial base station in next generation cellular networks , 2016, 2016 IEEE International Conference on Communications (ICC).

[16]  Xiaofeng Tao,et al.  Cooperative UAV Cluster-Assisted Terrestrial Cellular Networks for Ubiquitous Coverage , 2018, IEEE Journal on Selected Areas in Communications.

[17]  Walid Saad,et al.  Drone Small Cells in the Clouds: Design, Deployment and Performance Analysis , 2014, 2015 IEEE Global Communications Conference (GLOBECOM).

[18]  Halim Yanikomeroglu,et al.  On the Number and 3D Placement of Drone Base Stations in Wireless Cellular Networks , 2016, 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall).

[19]  Xianbin Cao,et al.  Proactive Drone-Cell Deployment: Overload Relief for a Cellular Network Under Flash Crowd Traffic , 2017, IEEE Transactions on Intelligent Transportation Systems.

[20]  Mahbub Hassan,et al.  Dynamic base station repositioning to improve spectral efficiency of drone small cells , 2017, 2017 IEEE 18th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM).

[21]  Andrey V. Savkin,et al.  An Algorithm of Reactive Collision Free 3-D Deployment of Networked Unmanned Aerial Vehicles for Surveillance and Monitoring , 2020, IEEE Transactions on Industrial Informatics.

[22]  H. Bruyninckx Mobile robots ∗ † , 2005 .

[23]  Abbas Jamalipour,et al.  Modeling air-to-ground path loss for low altitude platforms in urban environments , 2014, 2014 IEEE Global Communications Conference.

[24]  Chi Harold Liu,et al.  Energy-Efficient UAV Control for Effective and Fair Communication Coverage: A Deep Reinforcement Learning Approach , 2018, IEEE Journal on Selected Areas in Communications.

[25]  Mehdi Bennis,et al.  Drone Small Cells in the Clouds: Design, Deployment and Performance Analysis , 2014, GLOBECOM 2014.

[26]  Halim Yanikomeroglu,et al.  Strategic Densification With UAV-BSs in Cellular Networks , 2018, IEEE Wireless Communications Letters.

[27]  Mahbub Hassan,et al.  Understanding autonomous drone maneuverability for Internet of Things applications , 2017, 2017 IEEE 18th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM).

[28]  Mohsen Guizani,et al.  When Mobile Crowd Sensing Meets UAV: Energy-Efficient Task Assignment and Route Planning , 2018, IEEE Transactions on Communications.

[29]  Mehdi Bennis,et al.  UAV-Assisted Heterogeneous Networks for Capacity Enhancement , 2016, IEEE Communications Letters.

[30]  Andrey V. Savkin,et al.  On the Problem of Flying Robots Deployment to Improve Cellular User Experience , 2018, 2018 37th Chinese Control Conference (CCC).

[31]  Walid Saad,et al.  Efficient Deployment of Multiple Unmanned Aerial Vehicles for Optimal Wireless Coverage , 2016, IEEE Communications Letters.