|Table of Contents|

[1] Yan Feng, Zhou Tianxiang, Li Hao, et al. 3D non-stationary geometry-based stochastic model for unmannedaerial vehicle air-to-ground multi-input multi-output channels [J]. Journal of Southeast University (English Edition), 2022, 38 (4): 323-331. [doi:10.3969/j.issn.1003-7985.2022.04.001]
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3D non-stationary geometry-based stochastic model for unmannedaerial vehicle air-to-ground multi-input multi-output channels()
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
38
Issue:
2022 4
Page:
323-331
Research Field:
Information and Communication Engineering
Publishing date:
2022-12-20

Info

Title:
3D non-stationary geometry-based stochastic model for unmannedaerial vehicle air-to-ground multi-input multi-output channels
Author(s):
Yan Feng1;  2;  Zhou Tianxiang1;  Li Hao2;  Pang Jingming3;  Ding Kai2;  Xia Weiwei1;  Shen Lianfeng1
1National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China
2Science and Technology on Near-Surface Detection Laboratory, Wuxi 214035, China
3Jiangsu Zhongli Electronic Information Sci-Tech Co., Ltd., Changshu 215542, China
Keywords:
unmanned aerial vehicles(UAVs);  geometry-based stochastic model(GBSM);  air-to-ground channels
PACS:
TN929
DOI:
10.3969/j.issn.1003-7985.2022.04.001
Abstract:
A three-dimensional non-stationary geometry-based stochastic model for unmanned aerial vehicle(UAV)air-to-ground multi-input multi-output(MIMO)channels is proposed. The scatterers surrounding the UAV and ground station are assumed to be distributed on the surface of two cylinders in the proposed model. The impact of UAV rotations and accelerated motion is considered to describe channel non-stationarity. The computational methods of the corresponding time-variant parameters, such as UAV antenna array angles, time delays, and maximum Doppler frequencies, are theoretically deduced. The model is then used to derive channel statistical properties such as space-time correlation functions and Doppler power spectral density. Finally, numerical simulations are run to validate the channel’s statistical properties. The simulation results show that increasing the UAV and ground station accelerations can reduce the time correlation function and increase channel non-stationarity in the time domain. Furthermore, the UAV’s rotation significantly influences the spatial correlation function, with rolling having a greater influence than pitching. Similarly, the different directions of UAV movement significantly impact the Doppler power spectral density.

References:

[1] Mozaffari M, Saad W, Bennis M, et al. A tutorial on UAVs for wireless networks: Applications, challenges, and open problems[J]. IEEE Communications Surveys & Tutorials, 2019, 21(3): 2334-2360. DOI:10.1109/COMST.2019.2902862.
[2] Duo B, Li Y L, Hu H, et al. Joint robust 3D trajectory and communication design for dual-UAV enabled secure communications in probabilistic LoS channel[J]. Ad Hoc Networks, 2021, 121: 102592. DOI:10.1016/j.adhoc.2021.102592.
[3] Yan C X, Fu L G, Zhang J K, et al. A comprehensive survey on UAV communication channel modeling[J]. IEEE Access, 2019, 7: 107769-107792. DOI:10.1109/ACCESS.2019.2933173.
[4] Khawaja W, Guvenc I, Matolak D W, et al. A survey of air-to-ground propagation channel modeling for unmanned aerial vehicles[J]. IEEE Communications Surveys & Tutorials, 2019, 21(3): 2361-2391. DOI:10.1109/COMST.2019.2915069.
[5] Gao Z B, Liu B, Cheng Z P, et al. Marine mobile wireless channel modeling based on improved spatial partitioning ray tracing[J].China Communications, 2020, 17(3): 1-11. DOI:10.23919/JCC.2020.03.001.
[6] Zaman M A, Mamun S A, Gaffar M, et al. Modeling VHF air-to-ground multipath propagation channel and analyzing channel characteristics and BER performance[C]//2010 IEEE Region 8 International Conference on Computational Technologies in Electrical and Electronics Engineering. Irkutsk, Russia, 2010: 335-338. DOI:10.1109/SIBIRCON.2010.5555104.
[7] Jia R B, Li Y R, Cheng X, et al. 3D geometry-based UAV-MIMO channel modeling and simulation[J].China Communications, 2018, 15(12): 64-74. DOI:10.12676/j.cc.2018.12.005.
[8] Jiang H, Zhang Z C, Gui G. Three-dimensional non-stationary wideband geometry-based UAV channel model for A2G communication environments[J]. IEEE Access, 2019, 7: 26116-26122. DOI:10.1109/ACCESS.2019.2897431.
[9] Zeng L Z, Cheng X, Wang C X, et al. A 3D geometry-based stochastic channel model for UAV-MIMO channels[C]//2017 IEEE Wireless Communications and Networking Conference. San Francisco, CA, USA, 2017: 1-5. DOI:10.1109/WCNC.2017.7925794.
[10] Chang H T, Bian J, Wang C X, et al. A 3D non-stationary wideband GBSM for low-altitude UAV-to-ground V2V MIMO channels[J]. IEEE Access, 2019, 7: 70719-70732. DOI:10.1109/ACCESS.2019.2919790.
[11] Bian J, Wang C X, Liu Y, et al. 3D non-stationary wideband UAV-to-ground MIMO channel models based on aeronautic random mobility model[J]. IEEE Transactions on Vehicular Technology, 2021, 70(11): 11154-11168. DOI:10.1109/TVT.2021.3116953.
[12] Chang H T, Wang C X, Liu Y, et al. A novel nonstationary 6G UAV-to-ground wireless channel model with 3-D arbitrary trajectory changes[J].IEEE Internet of Things Journal, 2021, 8(12): 9865-9877. DOI:10.1109/JIOT.2020.3018479.
[13] Jiang H, Zhang Z C, Wang C X, et al. A novel 3D UAV channel model for A2G communication environments using AoD and AoA estimation algorithms[J]. IEEE Transactions on Communications, 2020, 68(11): 7232-7246. DOI:10.1109/TCOMM.2020.3011716.
[14] Ma Z F, Ai B, He R S, et al. Impact of UAV rotation on MIMO channel characterization for air-to-ground communication systems[J].IEEE Transactions on Vehicular Technology, 2020, 69(11): 12418-12431. DOI:10.1109/TVT.2020.3028301.
[15] Cheng X, Li Y R, Wang C X, et al. A 3-D geometry-based stochastic model for unmanned aerial vehicle MIMO ricean fading channels[J]. IEEE Internet of Things Journal, 2020, 7(9): 8674-8687. DOI:10.1109/JIOT.2020.2995707.
[16] Zeng L Z, Cheng X, Wang C X, et al. Second order statistics of non-isotropic UAV ricean fading channels[C]//2017 IEEE 86th Vehicular Technology Conference. Toronto, ON, Canada, 2017: 1-5. DOI:10.1109/VTCFall.2017.8287893.
[17] Zhang X, Cheng X. Second order statistics of simulation models for UAV-MIMO ricean fading channels[C]// 2019 IEEE International Conference on Communications. Shanghai, China, 2019: 1-6. DOI:10.1109/ICC.2019.8761565.
[18] Ma Z F, Ai B, He R S, et al. A wideband non-stationary air-to-air channel model for UAV communications[J].IEEE Transactions on Vehicular Technology, 2020, 69(2): 1214-1226. DOI:10.1109/TVT.2019.2961178.

Memo

Memo:
Biography: Yan Feng(1983—), male, doctor, associate professor, feng.yan@seu.edu.cn.
Foundation items: The Pre-Research Fund of Science and Technology on Near-Surface Detection Laboratory(No. 6142414190405, 6142414200505), the Specialized Development Foundation for the Achievement Transformation of Jiangsu Province(No. BA2019025).
Citation: Yan Feng, Zhou Tianxiang, Li Hao, et al.3D non-stationary geometry-based stochastic model for unmanned aerial vehicle air-to-ground multi-input multi-output channels[J].Journal of Southeast University(English Edition), 2022, 38(4):323-331.DOI:10.3969/j.issn.1003-7985.2022.04.001.
Last Update: 2022-12-20