Trajectory Planning of Multiple Dronecells in Vehicular Networks: A Reinforcement Learning Approach

被引：26

作者：

Samir, Moataz ^{[1
]}

Ebrahimi, Dariush ^{[2
]}

Assi, Chadi ^{[1
]}

Sharafeddine, Sanaa ^{[3
]}

Ghrayeb, Ali ^{[4
]}

机构：

[1] Concordia Institute for Information Systems Engineering, Concordia University, Montreal, H4V1S1, QC

[2] Department of Computer Science, Lakehead University, Thunder Bay, P7B 5E1, ON

[3] Department of Computer Science and Mathematics, Lebanese American University, Beirut

[4] Electrical and Computer Engineering Department, Texas AandM University at Qatar, Doha

来源：

IEEE Networking Letters | 2020年 / 2卷 / 01期

关键词：

Artificial intelligence; Coverage; Trajectory Planning; UAVs; Vehicular Networks;

D O I：

10.1109/LNET.2020.2966976

中图分类号：

学科分类号：

摘要：

The agility of unmanned aerial vehicles (UAVs) have been recently harnessed in developing potential solutions that provide seamless coverage for vehicles in areas with poor cellular infrastructure. In this letter, multiple UAVs are deployed to provide the needed cellular coverage to vehicles traveling with random speeds over a given highway segment. This letter minimizes the number of deployed UAVs and optimizes their trajectories to offer prevalent communication coverage to all vehicles crossing the highway segment while saving energy consumption of the UAVs. Due to varying traffic conditions on the highway, a reinforcement learning approach is utilized to govern the number of needed UAVs and their trajectories to serve the existing and newly arriving vehicles. Numerical results demonstrate the effectiveness of the proposed design and show that during the mission time, a minimum number of UAVs adapt their velocities in order to cover the vehicles. © 2019 IEEE.

引用

页码：14 / 18

页数：4

共 11 条

[1] Global Connected Car Market Opportunities and Forecasts, 2018-2025, (2018)
[2] Wu Q., Zeng Y., Zhang R., Joint trajectory and communication design for multi-UAV enabled wireless networks, IEEE Trans. Wireless Commun., 17, 3, pp. 2109-2121, (2018)
[3] Zeng Y., Xu X., Zhang R., Trajectory design for completion time minimization in UAV-enabled multicasting, IEEE Trans. Wireless Commun., 17, 4, pp. 2233-2246, (2018)
[4] Yang D., Wu Q., Zeng Y., Zhang R., Energy trade-off in ground-to-UAV communication via trajectory design, IEEE Trans. Veh. Technol., 67, 7, pp. 6721-6726, (2018)
[5] Mekikis P.-V., Antonopoulos A., Breaking the boundaries of aerial networks with charging stations, Proc. IEEE ICC, pp. 1-6, (2019)
[6] Samir M., Sharafeddine S., Assi C., Nguyen T.M., Ghrayeb A., Trajectory planning and resource allocation of multiple UAVs for data delivery in vehicular networks, IEEE Netw. Lett., 1, 3, pp. 107-110, (2019)
[7] Zhang Z., Mao G., Anderson B.D.O., Stochastic characterization of information propagation process in vehicular ad hoc networks, IEEE Trans. Intell. Transp. Syst., 15, 1, pp. 122-135, (2014)
[8] Zeng Y., Xu J., Zhang R., Energy minimization for wireless communication with rotary-wing UAV, IEEE Trans. Wireless Commun., 18, 4, pp. 2329-2345, (2019)
[9] Khabazian M., Ali M.K.M., A performance modeling of connectivity in vehicular ad hoc networks, IEEE Trans. Veh. Technol., 57, 4, pp. 2440-2450, (2008)
[10] Abboud K., Zhuang W., Stochastic analysis of a single-hop communication link in vehicular ad hoc networks, IEEE Trans. Intell. Transp. Syst., 15, 5, pp. 2297-2307, (2014)

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