Jittering Effects Analysis and Beam Training Design for UAV Millimeter Wave Communications

被引:57
作者
Wang, Wei [1 ]
Zhang, Wei [1 ]
机构
[1] Univ New South Wales, Sch Elect Engn & Telecommun, Sydney, NSW 2052, Australia
基金
澳大利亚研究理事会;
关键词
Unmanned aerial vehicles; Jitter; Wireless communication; Training; Millimeter wave communication; Directive antennas; Antenna arrays; UAV; jittering effects; mmWave communications; beam training; navigation; compressed sensing; TO-GROUND COMMUNICATION; CHANNEL ESTIMATION;
D O I
10.1109/TWC.2021.3118558
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Jittering effects significantly degrade the performance of UAV millimeter-wave (mmWave) communications. To investigate the impacts of UAV jitter on mmWave communications, we firstly model UAV mmWave channel based on the geometric relationship between element antennas of the uniform planar arrays (UPAs). Then, we extract the relationship between (I) UAV attitude angles & position coordinates and (II) angle of arrival (AoA) & angle of departure (AoD) of mmWave channel, and we also derive the distribution of AoA/AoD at UAV side from the random fluctuations of UAV attitude angles, i.e., UAV jitter. In beam training design, with the relationship between attitude angles and AoA/AoD, we propose to generate a rough estimate of AoA and AoD from UAV navigation information. Finally, with the rough AoA/AoD estimate, we develop a compressed sensing (CS) based beam training scheme with constrained sensing range as the fine AoA/AoD estimation. Particularly, we construct a partially random sensing matrix to narrow down the sensing range of CS-based beam training. Numerical results show that our proposed UAV beam training scheme assisted by navigation information can achieve better accuracy with reduced training length in AoA/AoD estimation and is thus more suitable for UAV mmWave communications under jittering effects.
引用
收藏
页码:3131 / 3146
页数:16
相关论文
共 48 条
[1]   Wind Measurement and Simulation Techniques in Multi-Rotor Small Unmanned Aerial Vehicles [J].
Abichandani, Pramod ;
Lobo, Deepan ;
Ford, Gabriel ;
Bucci, Donald ;
Kam, Moshe .
IEEE ACCESS, 2020, 8 :54910-54927
[2]  
[Anonymous], 2006, Fundamentals of Wireless Communication
[3]   Real-Time Wind Speed Estimation and Compensation for Improved Flight [J].
Arain, Bilal ;
Kendoul, Farid .
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2014, 50 (02) :1599-1606
[4]   Ultra Reliable UAV Communication Using Altitude and Cooperation Diversity [J].
Azari, Mohammad Mahdi ;
Rosas, Fernando ;
Chen, Kwang-Cheng ;
Pollin, Sofie .
IEEE TRANSACTIONS ON COMMUNICATIONS, 2018, 66 (01) :330-344
[5]   Impact of UAV Wobbling on the Air-to-Ground Wireless Channel [J].
Banagar, Morteza ;
Dhillon, Harpreet S. ;
Molisch, Andreas F. .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2020, 69 (11) :14025-14030
[6]   mmBAC: Location-aided mmWave Backhaul Management for UAV-based Aerial Cells [J].
Bertizzolo, Lorenzo ;
Polese, Michele ;
Bonati, Leonardo ;
Gosain, Abhimanyu ;
Zorzi, Michele ;
Melodia, Tommaso .
PROCEEDINGS OF THE 3RD ACM WORKSHOP ON MILLIMETER-WAVE NETWORKS AND SENSING SYSTEMS, MMNETS 2019, 2019, :7-12
[7]  
Cano J. Moyano, 2013, THESIS CHARLES 3 U M
[8]   Analytical Channel Models for Millimeter Wave UAV Networks Under Hovering Fluctuations [J].
Dabiri, Mohammad Taghi ;
Safi, Hossein ;
Parsaeefard, Saeedeh ;
Saad, Walid .
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2020, 19 (04) :2868-2883
[9]  
DJI, PHANT 4 PRO MAN PRO
[10]   Spatially Sparse Precoding in Millimeter Wave MIMO Systems [J].
El Ayach, Omar ;
Rajagopal, Sridhar ;
Abu-Surra, Shadi ;
Pi, Zhouyue ;
Heath, Robert W., Jr. .
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2014, 13 (03) :1499-1513