无人机空对地毫米波通信路径损耗预测

doi:10.3969/j.issn.0255-8297.2021.03.006

应用科学学报 ›› 2021, Vol. 39 ›› Issue (3): 398-397.doi: 10.3969/j.issn.0255-8297.2021.03.006

• 通信工程 • 上一篇

无人机空对地毫米波通信路径损耗预测

杨婧文¹, 朱秋明¹, 王健², 姚梦恬¹, 陈小敏¹, 仲伟志¹

1. 南京航空航天大学电磁频谱空间认知动态系统工业与信息化部重点实验室, 江苏南京 211106;
2. 中国电波传播研究所创新服务中心, 山东青岛 266107

收稿日期:2020-02-01 发布日期:2021-06-08
通信作者: 朱秋明，副教授，研究方向为无线信道建模。E-mail:zhuqiuming@nuaa.edu.cn E-mail:zhuqiuming@nuaa.edu.cn
基金资助:
国家重大科学仪器设备开发专项基金（No.61827801）资助

Path Loss Prediction for UAV-to-Ground Millimeter Wave Communications

YANG Jingwen¹, ZHU Qiuming¹, WANG Jian², YAO Mengtian¹, CHEN Xiaomin¹, ZHONG Weizhi¹

1. Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, Ministry of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, Jiangsu, China;
2. Innovation Service Center, China Research Institute of Radio Wave Propagation, Qingdao 266107, Shandong, China

Received:2020-02-01 Published:2021-06-08

摘要/Abstract

摘要： 针对无人机（unmanned aerial vehicle，UAV）空对地毫米波通信经历的复杂传播环境，综合考虑室内、室外和雨衰等多种影响因素，建立了一种UAV空对地复合传播路径损耗的预测模型。该模型采用楼房密度和高度参数描述城市、郊区和乡村等不同场景，结合UAV高度计算视距（line-of-sight，LoS）路径的出现概率，并获得UAV空对地传播损耗的统计平均值。针对大学校园中不同距离和高度的场景，利用射线跟踪理论分析方法对本文预测模型进行仿真验证。结果表明，该方法获得的统计预测结果与特定场景下射线跟踪确定的计算结果在3种高度下的最大误差分别为5.2 dB、4.8 dB和7.2 dB，说明所提方法可用于城市、郊区和乡村等场景UAV空对地毫米波传播损耗的预测及通信系统性能评估。

关键词: 无人机, 毫米波通信, 传播损耗, 视距概率, 射线跟踪

Abstract: For the complex propagation environment experienced by millimeter wave (mmWave) communications between unmanned aerial vehicle (UAV) to ground, considering the factors of outdoor path loss, indoor path loss, penetration loss, and rain attenuation, this paper presents a novel UAV-to-ground composite propagation loss prediction model. In the model, the density and height of buildings are adopted to depict different scenarios, such as urban, suburban, and rural areas. Further, light-of-sight (LoS) probability with respect to the UAV height is adopted to get the statistical average value of path loss. The proposed model is simulated and validated onto a campus scene at different distances and heights by using a ray tracing method. Comparison and analysis show that the results of the proposed model agree well with those of ray-tracing method, and the maximum errors of three specific scenarios are 5.2 dB, 4.8 dB, and 7.2 dB, respectively. This proves that the proposed model is potential in the prediction of propagation loss and the performance evaluation of UAV-to-ground mmWave communication systems.

Key words: unmanned aerial vehicle (UAV), millimeter wave communication, propagation loss, line-of-sight (LoS) probability, ray tracing

中图分类号:

TN928

杨婧文, 朱秋明, 王健, 姚梦恬, 陈小敏, 仲伟志. 无人机空对地毫米波通信路径损耗预测[J]. 应用科学学报, 2021, 39(3): 398-397.

YANG Jingwen, ZHU Qiuming, WANG Jian, YAO Mengtian, CHEN Xiaomin, ZHONG Weizhi. Path Loss Prediction for UAV-to-Ground Millimeter Wave Communications[J]. Journal of Applied Sciences, 2021, 39(3): 398-397.

参考文献

[1] Zhu Q M, Jiang K L, Chen X M, et al. A novel 3D non-stationary UAV-MIMO channel model and its statistical properties[J]. China Communication, 2018, 15(12): 147-158.
[2] Chen X M, Hu X J, Zhu Q M, et al. Channel modeling and performance analysis for UAV relay systems[J]. China Communications, 2018, 15(12): 89-97.
[3] Zhu Q M, Yang Y, Jiang K L, et al. A novel 3D non-stationary geometry-based MIMO channel model for UAV-ground communication systems[J]. IET Microwaves, Antennas and Propagation, 2019, 13(8): 1104-1112.
[4] Hur S, Baek S, Kim B, et al. Proposal on millimeter-wave channel modeling for 5G cellular system[J]. IEEE Journal of Selected Topics in Signal Processing, 2016, 10(3): 454-469.
[5] Sulyman A I, Alwarafy A, Maccartney G R, et al. Directional radio propagation path loss models for millimeter-wave wireless networks in the 28-, 60-, and 73-GHz bands[J]. IEEE Transactions on Wireless Communications, 2016, 15(10): 6939-6947.
[6] Maccartney G R, Rappaport T S. Rural macrocell path loss models for millimeter wave wireless communications[J]. IEEE Journal on Selected Areas in Communications, 2017, 35(7): 1663-1677.
[7] Rappaport T S, Xing Y, Maccartney G R, et al. Overview of millimeter wave communications for fifth-generation (5G) wireless networks-with a focus on propagation models[J]. IEEE Transactions on Antennas and Propagation, 2017, 65(12): 6213-6230.
[8] Zhang L, Zhao H, Hou S, et al. A survey on 5G millimeter wave communications for UAVassisted wireless networks[J]. IEEE Access, 2019: 1-41.
[9] Khawaja W, Guvenc I, Matolak D, et al. A survey of air-to-ground propagation channel modeling for unmanned aerial vehicles[J]. IEEE Communications Surveys and Tutorials, 2019: 1-33.
[10] Zeng Y, Lyu J, Zhang R. Cellular-connected UAV: potential, challenges, and promising technologies[J]. IEEE Wireless Communications, 2019, 26(1): 120-127.
[11] Cui Z, Briso C, Guan K, et al. Low-altitude UAV air-ground propagation channel measurement and analysis in a suburban environment at 3.9 GHz[J]. IET Microwaves, Antennas and Propagation, 2019, 13(9): 1503-1508.
[12] Cui Z, Briso C, Guan K, et al. Measurement-based modeling and analysis of UAV air-ground channels at 1 GHz and 4 GHz[J]. IEEE Antennas and Wireless Propagation Letters, 2019: 1-5.
[13] Lu C, Zhao Z, Wu Z, et al. A new rain attenuation prediction model for the earth-space links[J]. IEEE Transactions on Antennas and Propagation, 2018, 66(10): 5432-5442.
[14] Shayea I, Rahman T A, Azmi M H, et al. Real measurement study for rain rate and rain attenuation conducted over 26 GHz microwave 5G link system in Malaysia[J]. IEEE Access, 2018, 6: 19044-19064.
[15] Andrade F, Medeiros A, Luiz D S M. Short-term rain attenuation predictor for terrestrial links in tropical area[J]. IEEE Antennas and Wireless Propagation Letters, 2017, 16: 1325-1328.
[16] Bas C U, Wang R, Sangodoyin S, et al. Outdoor to indoor propagation channel measurements at 28 GHz[J]. IEEE Transactions on Wireless Communications, 2019, 18(3): 1477-1489.
[17] Qu Z, Zhang G, Cao H, et al. Stochastic dynamic modeling of rain attenuation: a survey[J]. China Communications, 2018, 15(3): 220-235.
[18] Ulaganathen K, Tharek A R, Islam R M, et al. Rain attenuation for 5G network in tropical region (Malaysia) for terrestrial link[C]//13th Malaysia International Conference on Communications (MICC), Johor Bahru, 2017: 35-38.
[19] Bai T, Vaze R, Heath R W. Analysis of blockage effects on urban cellular networks[J]. IEEE Transactions on Wireless Communications, 2014, 13(9): 5070-5083.

无人机空对地毫米波通信路径损耗预测

Path Loss Prediction for UAV-to-Ground Millimeter Wave Communications

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	马飞虎, 徐发东, 孙翠羽. 面向对象的无人机影像地物分类[J]. 应用科学学报, 2021, 39(2): 312-320.
[2]	张春森, 张会, 郭丙轩, 杜神斌, 张月莹. 顾及相机响应函数的无人机影像匀光匀色[J]. 应用科学学报, 2019, 37(6): 783-794.
[3]	季蕾, 樊春霞. 基于随机时延的多无人机编队控制方法[J]. 应用科学学报, 2019, 37(4): 551-564.
[4]	陈龙胜, 宁晓明. 四旋翼无人机预设性能非线性PI串级姿态控制[J]. 应用科学学报, 2019, 37(1): 137-150.
[5]	许忠雄, 邵瑰玮, 谢予星, 吴亮, 季铮. 基于编码标志的无人机电力巡检自主定位方法[J]. 应用科学学报, 2018, 36(5): 845-858.
[6]	张春森, 仇振国, 郭丙轩, 肖雄武, 朱师欢. 顾及空间邻接关系的无人机影像匹配并行算法[J]. 应用科学学报, 2017, 35(6): 775-785.
[7]	邓诗谦, 程万胜, 王恺. 旋翼无人机目标推力矢量最速趋近法[J]. 应用科学学报, 2017, 35(2): 244-256.
[8]	徐亚鹏, 苏成利, 孙小平. 四旋翼无人机飞行姿态的自适应反演滑模控制[J]. 应用科学学报, 2016, 34(3): 339-351.
[9]	张磊, 陆宇平, 殷明. 多传感器融合四旋翼协同控制算法及其实现[J]. 应用科学学报, 2016, 34(2): 190-202.
[10]	陈晓，潘韵天，武艺鸣，郑国莘. 地铁隧道内多径信道的统计特性[J]. 应用科学学报, 2015, 33(4): 389-398.
[11]	杜继永1，张凤鸣2，毛红保3，杨骥1，张超1. 未知环境下多UAV搜索的区域再入[J]. 应用科学学报, 2013, 31(3): 315-320.
[12]	李雪松1，李颖晖1，李霞2，李朝旭1，郭创1. 无人机鲁棒反推自适应编队导引控制设计[J]. 应用科学学报, 2012, 30(5): 552-558.
[13]	李猛，王道波，柏婷婷，盛守照. 基于蚁群优化算法和人工势场的无人机航迹规划[J]. 应用科学学报, 2012, 30(2): 215-220.
[14]	李雪松1，李颖晖1，李霞1，王志科2. 基于鲁棒时变卡尔曼滤波估计的无人机视觉编队[J]. 应用科学学报, 2011, 29(5): 545-550.
[15]	姚克明1，刘燕斌2，陆宇平2，谢启源2. 火星探测无人机建模与切换控制[J]. 应用科学学报, 2010, 28(6): 655-650.