Intelligent Deployment of Autonomous UAV Networks for Provisioning Communication Services

Written by Xuehe Wang, Xiao Zhang, and Lingjie Duan

An Unmanned Aerial Vehicle (UAV) network has emerged as a promising robotics technique to rapidly provide communication services to a geographical area out of coverage or capacity of the ground infrastructure. However, UAV-aided communication services (UCS) face challenges due to the UAV limitation in wireless coverage and energy storage.  Aware of such physical limitations, a future UAV network should be intelligent enough to self-plan trajectories and best service users. There are important issues regarding the UAV-user interaction for path planning, UAV-UAV cooperation for sustainable service provision, onboard energy allocation for balancing both hovering time and service capacity, and dynamic UCS pricing according to leftover energy and random demands. These networking and service management issues are largely overlooked in the literature. This article discusses intelligent solutions for the autonomous UCS deployment and operation.

Recent years have witnessed increasingly more exercises and commercial uses of Unmanned Aerial Vehicles (UAVs) as flying robotics for rapidly providing wireless coverage to ground users [1]. The static territorial base stations are deployed to meet the average traffic, and their networks lack the flexibility and agility to adjust to traffic variation and users’ dynamic needs for communication services. The flying cell site technology enabled by UAV rapidly expands the wireless coverage of the static territorial base stations on the ground, where UAVs serve as flying base stations to serve a geographical area (e.g., congested cell edge or disaster zone) out of the reach or capacity of the cellular networks.

The use of UAVs as flying base stations is attracting growing interest from researchers and the existing literature mainly focuses on technological issues for UAV network deployment, such as the air-to-ground transmission model [1], and wireless power transfer [2]. Despite their emerging applications, UAV-aided communication services (UCS) still face many new challenges in networking and service management. First, the endurance and performance of UAV systems are fundamentally limited by the on-board energy storage, which is practically finite due to the aircraft’s size and weight constraints. Moreover, how to dynamically price the capacity-limited UCS to ground users for profit-maximization is another issue. This is challenging under incomplete information about the mobile users’ randomness in arriving and their private valuations of buying UCS. Even more, the coverage radius of UAV is relatively small compared to the target area to be covered. Thus, it is important to consider how to design the UAV deployment scheme such that a number of UAVs cooperatively provide UCS. In the following, we will discuss two approaches to address the aforementioned networking and service management issues for UCS.


Sustainable Deployment via UAV-UAV Cooperation

In order to achieve maximum lifetime for cooperatively providing users with UCS, UAVs’ deployment energy should be minimized, which involves UAV-UAV cooperation in the deployment phase (e.g., which UAV to cover which part of the service area till full coverage is achieved by the whole UAV network). In [3, 4], the sustainable UAV deployment problem takes the UAVs’ four-dimensional heterogeneity in initial locations, hovering altitudes, deployment speeds, and wireless coverage into account.

In the min-max optimization problem, the network lifetime ends once a UAV stops working due to energy shortage, and UAVs only cooperate in the deployment phase for saving the bottleneck UAV for providing UCS. The objective is to minimize the maximum energy consumption among all UAVs under the constraint of full coverage over the target area, which is required to balance the deployment energy consumption among all UAVs and cooperatively optimize the energy bottleneck till reaching the full coverage of the target area.

may2021 5 1
Fig. 1
: Illustration of the optimal min-max deployment approach when n = 3 UAVs are initially co-located at the origin.


Figure 1 illustrates the optimal min-max deployment approach. As shown in Fig. 1, initially, we have n=3 UAVs (μ1, μ2, μ3) and start to dispatch one of them (i.e., the one with minimum energy consumption min Ei among the three UAVs) to just cover the furthest point β. Notice that a UAV μi with a large coverage radius ri or a small hovering height hi can save its deployment distance and should be chosen first. Suppose μ3 is selected, we dispatch it to the new position (β-ri,hi). Then the uncovered interval decreases from [0,β] to [0,β-2r3]. We continue to dispatch another unassigned UAV (e.g., followed by μ2) with minimum energy consumption as compared to μ1) to just cover endpoint β-2r3.  The approach ends until we assign enough UAVs to cover the whole target interval. Finally, we obtain the network lifetime by observing the residual energy as E0-max⁡{E1,E2,E3} where E0 is UAV’s initial energy storage.


Dynamic Pricing and Capacity Allocation of UCS

We will further study the economics of UCS provision after UAV deployment. As the UAV’s hovering in the air and its service provision to ground users are both energy-consuming, a longer hovering time helps meet more demands yet leaving less energy for servicing them. How to balance the hovering time and service capacity under the limited energy budget is critical to ensure the economic viability of UCS. Further, when hovering in a hotspot for a given period, how to dynamically price the capacity-limited UCS to ground users for profit-maximization is another issue. This is challenging under incomplete information about the mobile users’ randomness in arriving and their private valuations of buying UCS. Moreover, when facing multiple hotspot candidates with different user occurrence rates and flying distances, the optimal deployment of multiple UAVs to cooperatively serve the chosen hotspots needs to be studied. For maximizing the UAV company’s profit, a three-stage UCS provision model to study these economic issues has been proposed [5]:

  • Stage I: Deployment of UAVs to cooperatively cover heterogeneous hotspots.
  • Stage II: UAV’s energy allocation to balance hovering time and service capacity.
  • Stage III: Dynamic UCS pricing for each UAV over its hovering time.

These three stages following time sequence are interdependent for maximizing the UCS profit. By using the backward induction, we first propose an optimal dynamic pricing scheme under incomplete information by applying dynamic programming. It is proved that the UAV should ask for a higher UCS price if its leftover hovering time is longer, or its service capacity is smaller. If the hovering time is sufficiently large, the UAV’s expected profit approaches to that under complete user information, where a user’s service valuation is observed when he arrives. Then, an optimal threshold-based capacity allocation policy is proposed, which shows that as the user occurrence rate increases, a smaller hovering time or a larger service capacity should be allocated. An algorithm is proposed to find out the optimal UAV deployment based on Stage II and Stage III.



There are important networking and service management issues regarding the UAV-user interaction for service provision, optimal energy allocation for balancing both hovering time and dynamic UCS pricing. In this article, we studied the UAV network sustainability by minimizing the energy consumption cost when deploying the UAV network. Moreover, we analyzed the UAV’s dynamic pricing scheme under incomplete information, including random user arrivals and unknown service valuations, energy allocation to hovering time and service capacity, and optimal UAV deployment for UCS profit maximization.





  1. M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Drone small cells in the clouds: Design, deployment and performance analysis,” in Proc. of  IEEE Global Communications Conference (GLOBECOM), 2015.
  2. J. Xu, Y. Zeng, and R. Zhang, “UAV-enabled wireless power transfer: Trajectory design and energy region characterization,” IEEE Transactions on Wireless Communications, vol. 17, no. 8, pp. 5092–5106, 2018.
  3. X. Zhang and L. Duan, “Optimization of emergency UAV deployment for providing wireless coverage,” in Proc. of  IEEE Global Communications Conference (GLOBECOM), 2017.
  4. X. Zhang and L. Duan, “Fast deployment of UAV networks   for optimal wireless coverage,” IEEE Transactions on Mobile Computing, vol. 18, no. 3, pp. 588–601, 2019.
  5. X. Wang and L. Duan, “Economic Analysis of Unmanned Aerial Vehicle (UAV) Provided Mobile Services,” IEEE Trans- actions on Mobile Computing, 2020.


This article was edited by Wei Zhang

For a downloadable copy of the June 2021 eNewsletter which includes this article, please visit the IEEE Smart Cities Resource Center.

Xuehe Wang (S’15-M’16) received her Ph.D. degree in electrical and electronic engineering from Nanyang Technological University, Singapore in 2016. She is an Associate Professor with School of Artificial Intelligence, Sun Yat-sen University, China. She was an Assistant Professor with Infocomm Technology Cluster, Singapore Institute of Technology from 2019-2011, and postdoctoral research fellow with the Pillar of Engineering Systems and Design, Singapore University of Technology and Design from 2015-2019. Her research interests cover transportation, control theory, network economics and game theory.
Xiao Zhang (Member, IEEE) received the B.Eng. and M.Eng. degrees from the South-Central University for Nationalities, Wuhan, China, in 2009 and 2011 respectively, and the Ph.D. degree from Department of Computer Science in City University of Hong Kong, Hong Kong, 2016. He was a visiting scholar with Utah State University, Utah, USA and University of Lethbridge, Alberta, Canada. During 2016-2019, he was a Postdoc Research Fellow at Singapore University of Technology and Design. Currently, he is associate professor with the College of Computer Science, South-Central University for Nationalities, China. His research interests include wireless and UAV networking, algorithms design and analysis, and combinatorial optimization. He is an Editor of Frontiers in Space Technologies and Frontiers in Communications and Networks. He also served as a TPC member of IEEE GLOBECOM 2020, IEEE WCNC 2020, 2021, IFIP Networking 2021, and reviewer of IEEE Journal on Selected Areas in Communications, IEEE/ACM Transactions on Networking et al. He was selected in the Hubei Province High-Level Talents Programme in 2020.
Lingjie DUAN
Lingjie Duan (S’09-M’12-SM’17) received the Ph.D. degree from The Chinese University of Hong Kong in 2012. He is an Associate Professor of Engineering Systems and Design with the Singapore University of Technology and Design (SUTD). In 2011, he was a Visiting Scholar at University of California at Berkeley, Berkeley, CA, USA. His research interests include network economics and game theory, cognitive communications, UAV networking, and energy harvesting wireless communications. He received the SUTD Excellence in Research Award in 2016 and the 10th IEEE ComSoc Asia-Pacific Outstanding Young Researcher Award in 2015. He was also the finalist of the Hong Kong Young Scientist Award under Engineering Science track in 2014. He is now an Editor of IEEE Transactions on Wireless Communications. He was an Editor of IEEE Communications Surveys and Tutorials. He also served as a Guest Editor of the IEEE Journal on Selected Areas in Communications Special Issue on Human-in-the-Loop Mobile Networks, as well as IEEE Wireless Communications Magazine.

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