Online Trajectory Optimization for the UAV-Mounted Base Stations

Guangchi ZHANG; Yulin YAN; Miao CUI; Wei CHEN; Jing ZHANG

doi:10.11999/JEIT200525

Volume 43 Issue 12

Dec. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(12): 3605-3611

Guangchi ZHANG, Yulin YAN, Miao CUI, Wei CHEN, Jing ZHANG. Online Trajectory Optimization for the UAV-Mounted Base Stations[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3605-3611. doi: 10.11999/JEIT200525

Citation:

Guangchi ZHANG, Yulin YAN, Miao CUI, Wei CHEN, Jing ZHANG. Online Trajectory Optimization for the UAV-Mounted Base Stations[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3605-3611. doi: 10.11999/JEIT200525

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Online Trajectory Optimization for the UAV-Mounted Base Stations

doi: 10.11999/JEIT200525

1.
School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
2.
Institute of Environmental Geology Exploration of Guangdong Province, Guangzhou 510080, China
3.
China Academic of Electronics and Information Technology, Beijing 100043, China

Funds: The Science and Technology Plan Project of Guangdong Province (2017B090909006, 2019B010119001, 2020A050515010, 2021A0505030015), The Special Support Plan for High-Level Talents of Guangdong Province (2019TQ05X409)

Received Date: 2020-06-29
Rev Recd Date: 2021-06-07

Available Online: 2021-07-13

Publish Date: 2021-12-21

Abstract

Abstract

Considering dealing with the problem of random and dynamic communication requests of ground users in a UAV(Unmanned Aerial Vehicle) mounted base station communication system, which can not be tackled by an offline trajectory design scheme, an online trajectory optimization algorithm is proposed for the UAV-mounted base station. In the considered system, a single UAV is utilized as an aerial base station to provide wireless communication service to two ground users. The problem of minimizing the average communication delay of the ground users via optimizing the UAV’s trajectory is considered. First, it is shown that the problem can be casted as a Markov Decision Process (MDP), and then the delay of one single communication is introduced into the action value function. Finally, the Monte Carlo and Q-Learning algorithms from the reinforcement learning technology are respectively adopted to realize the online trajectory optimization. Simulation results show that the proposed algorithm outperforms the “fixed position” and “greedy algorithm” schemes.
- Unmanned Aerial Vehicle (UAV) communication,
- Online trajectory optimization,
- Average delay minimization,
- Reinforcement learning

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References(16)

References

[1]	ZENG Yong, ZHANG Rui, and LIM T J. Wireless communications with unmanned aerial vehicles: Opportunities and challenges[J]. IEEE Communications Magazine, 2016, 54(5): 36–42. doi: 10.1109/MCOM.2016.7470933
[2]	Paving the path to 5G: Optimizing commercial LTE networks for drone communication[EB/OL]. https://www.qualcomm.com/news/onq/2016/09/06/paving-path-5g-optimizing-commercial-lte-networks-drone-communication, 2016.
[3]	ZHANG Guangchi, WU Qingqing, CUI Miao, et al. Securing UAV communications via joint trajectory and power control[J]. IEEE Transactions on Wireless Communications, 2019, 18(2): 1376–1389. doi: 10.1109/TWC.2019.2892461
[4]	WU Qingqing, ZENG Yong, and ZHANG Rui. Joint trajectory and communication design for multi-UAV enabled wireless networks[J]. IEEE Transactions on Wireless Communications, 2018, 17(3): 2109–2121. doi: 10.1109/TWC.2017.2789293
[5]	ZENG Yong, ZHANG Rui, and LIM T J. Throughput maximization for UAV-enabled mobile relaying systems[J]. IEEE Transactions on Communications, 2016, 64(12): 4983–4996. doi: 10.1109/TCOMM.2016.2611512
[6]	ZENG Yong, LYU Jiangbin, and ZHANG Rui. Cellular-connected UAV: Potential, challenges, and promising technologies[J]. IEEE Wireless Communications, 2019, 26(1): 120–127. doi: 10.1109/MWC.2018.1800023
[7]	LYU Jiangbin, ZENG Yong, ZHANG Rui, et al. Placement optimization of UAV-mounted mobile base stations[J]. IEEE Communications Letters, 2017, 21(3): 604–607. doi: 10.1109/LCOMM.2016.2633248
[8]	ZHAN Cheng, ZENG Yong, and ZHANG Rui. Energy-efficient data collection in UAV enabled wireless sensor network[J]. IEEE Wireless Communications Letters, 2018, 7(3): 328–331. doi: 10.1109/LWC.2017.2776922
[9]	ZHANG Guangchi, YAN Haiqiang, ZENG Yong, et al. Trajectory optimization and power allocation for multi-hop UAV relaying communications[J]. IEEE Access, 2018, 6: 48566–48576. doi: 10.1109/ACCESS.2018.2868117
[10]	ZENG Yong and XU Xiaoli. Path design for cellular-connected UAV with reinforcement learning[EB/OL]. http://arxiv.org/abs/1905.03440, 2019.
[11]	黄长强, 赵克新, 韩邦杰, 等. 一种近似动态规划的无人机机动决策方法[J]. 电子与信息学报, 2018, 40(10): 2447–2452. doi: 10.11999/JEIT180068 HUANG Changqiang, ZHAO Kexin, HAN Bangjie, et al. Maneuvering decision-making method of UAV based on approximate dynamic programming[J]. Journal of Electronics &Information Technology, 2018, 40(10): 2447–2452. doi: 10.11999/JEIT180068
[12]	BLISS M and MICHELUSI N. Trajectory optimization for rotary-wing UAVs in wireless networks with random requests[EB/OL]. http://arxiv.org/abs/1905.01755, 2019.
[13]	SUTTON R S and BARTO A G. Reinforcement Learning: An Introduction[M]. 2nd ed. Cambridge: MIT Press, 2018: 1–130.
[14]	LIU Xiao, LIU Yuanwei, and CHEN Yue. Reinforcement learning in multiple-UAV networks: Deployment and movement design[J]. IEEE Transactions on Vehicular Technology, 2019, 68(8): 8036–8049. doi: 10.1109/TVT.2019.2922849
[15]	KHAMIDEHI B and SOUSA E S. Reinforcement learning-based trajectory design for the aerial base stations[EB/OL]. https://arxiv.org/abs/1906.09550, 2019.
[16]	ZENG Yong and ZHANG Rui. Energy-efficient UAV communication with trajectory optimization[J]. IEEE Transactions on Wireless Communications, 2017, 16(6): 3747–3760. doi: 10.1109/TWC.2017.2688328