A Joint Optimization Method for Trajectory and Power of Unmanned Aerial Vehicle assisted Over-the-Air Computation

LI Song; LI Jiaqi; WANG Bowen; CHEN Ruirui; SUN Yanjing; ZHANG Xiaoguang

doi:10.11999/JEIT230917

Article Contents

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

LI Song, LI Jiaqi, WANG Bowen, CHEN Ruirui, SUN Yanjing, ZHANG Xiaoguang. A Joint Optimization Method for Trajectory and Power of Unmanned Aerial Vehicle assisted Over-the-Air Computation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230917

Citation:

LI Song, LI Jiaqi, WANG Bowen, CHEN Ruirui, SUN Yanjing, ZHANG Xiaoguang. A Joint Optimization Method for Trajectory and Power of Unmanned Aerial Vehicle assisted Over-the-Air Computation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230917

Citation:

LI Song, LI Jiaqi, WANG Bowen, CHEN Ruirui, SUN Yanjing, ZHANG Xiaoguang. A Joint Optimization Method for Trajectory and Power of Unmanned Aerial Vehicle assisted Over-the-Air Computation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230917

PDF( 1382 KB)

A Joint Optimization Method for Trajectory and Power of Unmanned Aerial Vehicle assisted Over-the-Air Computation

doi: 10.11999/JEIT230917

LI Song^{1, 2},
LI Jiaqi¹,
WANG Bowen¹,
CHEN Ruirui¹,
SUN Yanjing^{1, 2},
ZHANG Xiaoguang^{1
,
,}

1.
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
2.
Xuzhou Engineering Research Center of Intelligent Industry Safety and Emergency Collaboration, Xuzhou 221116, China

Funds: The National Natural Science Foundation of China (62071472, 62101556), The Fundamental Research Funds for the Central Universities (2020ZDPYMS26), Natural Science Foundation of Jiangsu Province of China (BK20200650, BK20210489), Future Network Research Foundation of Jiangsu Province (FNSRFP-2021-YB-12)

Received Date: 2023-08-23
Rev Recd Date: 2023-12-05

Available Online: 2023-12-13

Abstract

Abstract

The Unmanned Aerial Vehicle (UAV)assisted over-the-Air Computation(AirComp) system provides an effective solution for the fast aggregation of large-scale and distributed data. In this paper, a joint trajectory planning and power optimization method through UAV-assisted AirComp system is investigated. As a mobile base station, UAV is used to optimize the mean square error of the aggregated data of the AirComp system by adjusting its trajectory and transmitting power of the ground sensors. Under the limitations of UAV trajectory and sensor power, the UAV flight trajectory, the scaling factor and sensor power are jointly optmized to minimize the time-averaged mean square error. Based on the block coordinate descent and successive convex approximation methods, the joint optimization algorithm of UAV flight trajectory and power is proposed. Simulation results verify the performance of the proposed algorithm.
- Over-the-Air Computation (AirComp),
- Trajectory planning,
- Power control,
- Unmanned Aerial Vehicle (UAV)

FullText(HTML)

References(17)

References

[1]	陈新颖, 盛敏, 李博, 等. 面向6G的无人机通信综述[J]. 电子与信息学报, 2022, 44(3): 781–789. doi: 10.11999/JEIT210789 CHEN Xinying, SHENG Min, LI Bo, et al. Survey on unmanned aerial vehicle communications for 6G[J]. Journal of Electronics & Information Technology, 2022, 44(3): 781–789. doi: 10.11999/JEIT210789
[2]	ZHAN Cheng and ZENG Yong. Completion time minimization for multi-UAV-enabled data collection[J]. IEEE Transactions on Wireless Communications, 2019, 18(10): 4859–4872. doi: 10.1109/TWC.2019.2930190
[3]	LI Mushu, CHENG Nan, GAO Jie, et al. Energy-efficient UAV-assisted mobile edge computing: Resource allocation and trajectory optimization[J]. IEEE Transactions on Vehicular Technology, 2020, 69(3): 3424–3438. doi: 10.1109/TVT.2020.2968343
[4]	CHAKARESKI J, NAQVI S, MASTRONARDE N, et al. An energy efficient framework for UAV-assisted millimeter wave 5G heterogeneous cellular networks[J]. IEEE Transactions on Green Communications and Networking, 2019, 3(1): 37–44. doi: 10.1109/TGCN.2019.2892141
[5]	SHEN Chao, CHANG T H, GONG Jie, et al. Multi-UAV interference coordination via joint trajectory and power control[J]. IEEE Transactions on Signal Processing, 2020, 68: 843–858. doi: 10.1109/TSP.2020.2967146
[6]	SAMIR M, SHARAFEDDINE S, ASSI C M, et al. UAV trajectory planning for data collection from time-constrained IoT devices[J]. IEEE Transactions on Wireless Communications, 2020, 19(1): 34–46. doi: 10.1109/TWC.2019.2940447
[7]	ZHU Guangxu, XU Jie, HUANG Kaibin, et al. Over-the-air computing for wireless data aggregation in massive IoT[J]. IEEE Wireless Communications, 2021, 28(4): 57–65. doi: 10.1109/MWC.011.2000467
[8]	GOLDENBAUM M, BOCHE H, and STAŃCZAK S. Harnessing interference for analog function computation in wireless sensor networks[J]. IEEE Transactions on Signal Processing, 2013, 61(20): 4893–4906. doi: 10.1109/TSP.2013.2272921
[9]	CAO Xiaowen, ZHU Guangxu, XU Jie, et al. Optimized power control for over-the-air computation in fading channels[J]. IEEE Transactions on Wireless Communications, 2020, 19(11): 7498–7513. doi: 10.1109/TWC.2020.3012287
[10]	ZHU Guangxu and HUANG Kaibin. MIMO over-the-air computation for high-mobility multimodal sensing[J]. IEEE Internet of Things Journal, 2019, 6(4): 6089–6103. doi: 10.1109/JIOT.2018.2871070
[11]	JIANG Miao, LI Yiqing, ZHANG Guangchi, et al. Joint beamforming optimization in multi-relay assisted MIMO over-the-air computation for multi-modal sensing data aggregation[J]. IEEE Communications Letters, 2021, 25(12): 3937–3941. doi: 10.1109/LCOMM.2021.3120182
[12]	ZHAI Xiongfei, CHEN Xihan, XU Jie, et al. Hybrid beamforming for massive MIMO over-the-air computation[J]. IEEE Transactions on Communications, 2021, 69(4): 2737–2751. doi: 10.1109/TCOMM.2021.3051397
[13]	YANG Kai, JIANG Tao, SHI Yuanming, et al. Federated learning via over-the-air computation[J]. IEEE Transactions on Wireless Communications, 2020, 19(3): 2022–2035. doi: 10.1109/TWC.2019.2961673
[14]	YOO T and GOLDSMITH A. Capacity and power allocation for fading MIMO channels with channel estimation error[J]. IEEE Transactions on Information Theory, 2006, 52(5): 2203–2214. doi: 10.1109/TIT.2006.872984
[15]	YU D, PARK S H, SIMEONE O, et al. Optimizing over-the-air computation in IRS-aided C-RAN systems[C]. 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). Atlanta, USA, 2020: 1–5.
[16]	JUNG H and KO S W. Performance analysis of UAV-enabled over-the-air computation under imperfect channel estimation[J]. IEEE Wireless Communications Letters, 2022, 11(3): 438–442. doi: 10.1109/LWC.2021.3130002
[17]	ZHU Guangxu, DU Yuqing, GÜNDÜZ D, et al. One-bit over-the-air aggregation for communication-efficient federated edge learning: Design and convergence analysis[J]. IEEE Transactions on Wireless Communications, 2021, 20(3): 2120–2135. doi: 10.1109/TWC.2020.3039309