Research on Resource Allocation and Trajectory Optimization of a Multitask Unmanned Aerial Vehicles Assisted Communication Network

PEI Errong; LOU Yuhan; LI Yonggang; LI Wei

doi:10.11999/JEIT230974

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2022 >

PEI Errong, LOU Yuhan, LI Yonggang, LI Wei. Research on Resource Allocation and Trajectory Optimization of a Multitask Unmanned Aerial Vehicles Assisted Communication Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230974

Citation:

PEI Errong, LOU Yuhan, LI Yonggang, LI Wei. Research on Resource Allocation and Trajectory Optimization of a Multitask Unmanned Aerial Vehicles Assisted Communication Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230974

Citation:

PEI Errong, LOU Yuhan, LI Yonggang, LI Wei. Research on Resource Allocation and Trajectory Optimization of a Multitask Unmanned Aerial Vehicles Assisted Communication Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230974

PDF( 3160 KB)

Research on Resource Allocation and Trajectory Optimization of a Multitask Unmanned Aerial Vehicles Assisted Communication Network

doi: 10.11999/JEIT230974

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Chongqing Jinmei Communication Co., Ltd., Chongqing 400035, China

Funds: The National Natural Science Foundation of China (62071077), Chongqing Chengyu Science and Technology Innovation Project (KJCXZD2020026)

Received Date: 2023-09-06
Rev Recd Date: 2024-01-25

Available Online: 2024-02-27

Abstract

Abstract

Unmanned Aerial Vehicles (UAV) loaded with various payloads can achieve multiple tasks such as sensing, communication, and computing, and are often deployed in fields such as Data Acquisition (DA) and auxiliary computing. However, so far, the vast majority of research has only focused on single function drone resource allocation and trajectory optimization, and the problem of multi task oriented drone resource allocation and trajectory optimization has not been solved yet. Therefore, an allocation strategy for optimizing drone communication network resources is proposed that comprehensively considers drone data acquisition, data broadcasting, and computing task offloading. The aim is to maximize user offloading by jointly optimizing transmission duty cycle, user transmission power, and drone trajectory, while meeting the real-time broadcast of target location data collection. In order to solve the problem of multivariable coupled optimization, an efficient iterative optimization algorithm based on Block Coordinate Descent (BCD) and Successive Convex Approximate (SCA) is proposed. The coupled optimization problem is decomposed into three sub problems for iterative optimization. Finally, a large number of simulation results show that the algorithm outperforms other testing schemes in terms of fairness and total offloading computation.
- Unmanned Aerial Vehicle (UAV) communication,
- Mobile Edge Computing (MEC),
- Data acquisition,
- Convex optimization

FullText(HTML)

References(18)

References

[1]	VAN HUYNH D, DO-DUY T, NGUYEN L D, et al. Real-time optimized path planning and energy consumption for data collection in unmanned Ariel vehicles-aided intelligent wireless sensing[J]. IEEE Transactions on Industrial Informatics, 2022, 18(4): 2753–2761. doi: 10.1109/TII.2021.3114358
[2]	YU Zhe, GONG Yanmin, GONG Shimin, et al. Joint task offloading and resource allocation in UAV-enabled mobile edge computing[J]. IEEE Internet of Things Journal, 2020, 7(4): 3147–3159. doi: 10.1109/JIOT.2020.2965898
[3]	OLLERO A, TOGNON M, SUAREZ A, et al. Past, present, and future of aerial robotic manipulators[J]. IEEE Transactions on Robotics, 2022, 38(1): 626–645. doi: 10.1109/TRO.2021.3084395
[4]	LIU Yi, NIE Jiangtian, LI Xuandi, et al. Federated learning in the sky: Aerial-ground air quality sensing framework with UAV swarms[J]. IEEE Internet of Things Journal, 2021, 8(12): 9827–9837. doi: 10.1109/JIOT.2020.3021006
[5]	ZENG Yao and TANG Jianhua. MEC-assisted real-time data acquisition and processing for UAV with general missions[J]. IEEE Transactions on Vehicular Technology, 2023, 72(1): 1058–1072. doi: 10.1109/TVT.2022.3203704
[6]	YANG Zheyuan, BI Suzhi, and ZHANG Y J A. Online trajectory and resource optimization for stochastic UAV-enabled MEC systems[J]. IEEE Transactions on Wireless Communications, 2022, 21(7): 5629–5643. doi: 10.1109/TWC.2022.3142365
[7]	ZHANG Shuhang, ZHANG Hongliang, HAN Zhu, et al. Age of information in a cellular internet of UAVs: Sensing and communication trade-off design[J]. IEEE Transactions on Wireless Communications, 2020, 19(10): 6578–6592. doi: 10.1109/TWC.2020.3004162
[8]	LI Tianyang, LENG Supeng, WANG Zhihong, et al. Intelligent resource allocation schemes for UAV-swarm-based cooperative sensing[J]. IEEE Internet of Things Journal, 2022, 9(21): 21570–21582. doi: 10.1109/JIOT.2022.3183099
[9]	MENG Kaitao, HE Xiaofan, WU Qingqing, et al. Multi-UAV collaborative sensing and communication: Joint task allocation and power optimization[J]. IEEE Transactions on Wireless Communications, 2023, 22(6): 4232–4246. doi: 10.1109/TWC.2022.3224143
[10]	LYU Liang, ZENG Fanzi, XIAO Zhu, et al. Computation bits maximization in UAV-enabled mobile-edge computing system[J]. IEEE Internet of Things Journal, 2022, 9(13): 10640–10651. doi: 10.1109/JIOT.2021.3123429
[11]	NIE Yiwen, ZHAO Junhui, GAO Feifei, et al. Semi-distributed resource management in UAV-aided MEC systems: A multi-agent federated reinforcement learning approach[J]. IEEE Transactions on Vehicular Technology, 2021, 70(12): 13162–13173. doi: 10.1109/TVT.2021.3118446
[12]	LIANG Tianhao, LIU Wentao, YANG Jiayan, et al. Age of information based scheduling for UAV aided emergency communication networks[C]. ICC 2022-IEEE International Conference on Communications, Seoul, Republic of Korea, 2022: 5128–5133.
[13]	ZHANG Liang and ANSARI N. Latency-aware IoT service provisioning in UAV-aided mobile-edge computing networks[J]. IEEE Internet of Things Journal, 2020, 7(10): 10573–10580. doi: 10.1109/JIOT.2020.3005117
[14]	HU Zhenzhen, ZENG Fanzi, XIAO Zhu, et al. Computation efficiency maximization and QoE-provisioning in UAV-enabled MEC communication systems[J]. IEEE Transactions on Network Science and Engineering, 2021, 8(2): 1630–1645. doi: 10.1109/TNSE.2021.3068123
[15]	SUN Mengying, XU Xiaodong, QIN Xiaoqi, et al. AoI-energy-aware UAV-assisted data collection for IoT networks: A deep reinforcement learning method[J]. IEEE Internet of Things Journal, 2021, 8(24): 17275–17289. doi: 10.1109/JIOT.2021.3078701
[16]	LU Weidang, DING Yu, GAO Yuan, et al. Secure NOMA-based UAV-MEC network towards a flying eavesdropper[J]. IEEE Transactions on Communications, 2022, 70(5): 3364–3376. doi: 10.1109/TCOMM.2022.3159703
[17]	LU Weidang, DING Yu, GAO Yuan, et al. Resource and trajectory optimization for secure communications in dual unmanned aerial vehicle mobile edge computing systems[J]. IEEE Transactions on Industrial Informatics, 2022, 18(4): 2704–2713. doi: 10.1109/TII.2021.3087726
[18]	裴二荣, 陈新虎, 陈琪美, 等. 基于全频谱共享的三维轨迹和功率优化方法[J]. 电子与信息学报, 2024, 3(46): 835–847. PEI Errong, CHEN Xinhu, CHEN Qimei, et al. 3D trajectory and power optimization method based on full spectrum sharing[J]. Journal of Electronics & Information Technology, 2024, 3(46): 835–847.