A Method for Deploying the Minimal Number of UAV Base Stations in Cellular Networks

Hailong Huang; Chao Huang; Dazhong Ma

doi:10.1109/JAS.2019.1911813

Volume 7 Issue 2

Mar. 2020

IEEE/CAA Journal of Automatica Sinica

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Hailong Huang, Chao Huang and Dazhong Ma, "A Method for Deploying the Minimal Number of UAV Base Stations in Cellular Networks," IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 559-567, Mar. 2020. doi: 10.1109/JAS.2019.1911813

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Hailong Huang, Chao Huang and Dazhong Ma, "A Method for Deploying the Minimal Number of UAV Base Stations in Cellular Networks," IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 559-567, Mar. 2020. doi: 10.1109/JAS.2019.1911813

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A Method for Deploying the Minimal Number of UAV Base Stations in Cellular Networks

doi: 10.1109/JAS.2019.1911813

College of Information Science and Engineering, Northeastern University, Shenyang 110004, China

Funds: This work was supported by the National Natural Science Foundation of China (61903076, 61773109) and Liaoning Revitalization Talents Program (XLYC1807009)

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Author Bio:
Hailong Huang received the B.Sc. degree in automation, from China University of Petroleum in 2012, and received the Ph.D. degree in systems and control from the University of New South Wales, Australia, in 2018. He is a Postdoctoral Researcher with the College of Information Science and Engineering at Northeastern University. His current research interests include wireless sensor networks, networking protocols, and guidance, navigation, and control of mobile robots

Chao Huang received the B.E. degree in automation from China University of Petroleum. She received the Ph.D. degree in automotive control and applications from University of Wollongong, Australia, in 2018. She is a Postdoctoral Researcher with the College of Information Science and Engineering at Northeastern University. Her current research interests include fault tolerant control theory and optimization

Dazhong Ma received the B.S. degree in automation in 2004 and the Ph.D. degree in control theory and control engineering in 2011, from Northeastern University. He is currently an Associate Professor with Northeastern University. His current research interests include fault diagnosis, fault-tolerant control, energy management systems, control and optimization of distributed generation systems, microgrids, and energy internet
Corresponding author: The authors are with the College of Information Science and Engineering, Northeastern University, Shenyang 110004, China (e-mail: huanghailong@mail.neu.edu.cn; ch449@uowmail.edu.au; madazhong@ise.neu.edu.cn)
¹ A supplier defined constant.
² The definition of vertex is provided in Fig. 1.
³ It is worth noticing that the range of n shown in (5) is to cover M street points instead of \begin{document}$ \alpha $\end{document} percent of UEs in the area. Further, it is based on the Assumption 1, which does not always hold in practical street graphs. Thus, it is only a rough range. However, we claim that this rough estimation is still reasonable in our problem setting. Firstly, the lower and upper bounds are theoretical results and n is unlikely to take either of them. Secondly, in practice the network supplier may expect \begin{document}$ \alpha $\end{document} to be close to 100%, e.g., 98%, which does not influence the bounds significantly.
⁴ When looking for the position for the first UAV-BS, constraints (2) and (3) are not applied.
Received Date: 2019-05-24
Revised Date: 2019-08-01
Accepted Date: 2019-08-12

Available Online: 2019-10-21

Abstract

Abstract

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.
- Blanket coverage,
- cellular networks,
- coverage control,
- proactive deployment,
- unmanned aerial vehicles (UAV)

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¹ A supplier defined constant.
² The definition of vertex is provided in Fig. 1.
³ It is worth noticing that the range of n shown in (5) is to cover M street points instead of $ \alpha $ percent of UEs in the area. Further, it is based on the Assumption 1, which does not always hold in practical street graphs. Thus, it is only a rough range. However, we claim that this rough estimation is still reasonable in our problem setting. Firstly, the lower and upper bounds are theoretical results and n is unlikely to take either of them. Secondly, in practice the network supplier may expect $ \alpha $ to be close to 100%, e.g., 98%, which does not influence the bounds significantly.
⁴ When looking for the position for the first UAV-BS, constraints (2) and (3) are not applied.

References(32)

References

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We mathematically formulate an optimization problem which minimizes the number of UAV-BSs subject to the three types of constraints: coverage, downlink rate, and the charging requirement.
We propose a solution to the formulated problem which involves the estimation of the bounds of the number of UAVBSs and a coverage maximizing algorithm to place UAV-BSs.
The convergence of such a coverage maximizing algorithm is proved.

A Method for Deploying the Minimal Number of UAV Base Stations in Cellular Networks

doi: 10.1109/JAS.2019.1911813

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