Multi-layer Unmanned Aerial Vehicle Networks: Modeling and Performance Analysis

被引:60
作者
Kim, Dongsun [1 ]
Lee, Jemin [1 ]
Quek, Tony Q. S. [2 ]
机构
[1] Daegu Gyeongbuk Inst Sci & Technol, Dept Informat & Commun Engn, Daegu 42988, South Korea
[2] Singapore Univ Technol & Design, Informat Syst Technol & Design Pillar, Singapore 487372, Singapore
基金
新加坡国家研究基金会;
关键词
Aerial networks; multiple network layer; unmanned aerial vehicles; stochastic geometry; line of sight (LoS) probability; ASSISTED CELLULAR NETWORKS; COMMUNICATION; LOS;
D O I
10.1109/TWC.2019.2944378
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
In this paper, we establish a foundation for the multi-layer aerial networks (MANs), which are modeled as K layer aerial networks (ANs), where each layer has unmanned aerial vehicles (UAVs) with different densities, floating altitudes, and transmission power. To make the framework applicable for various scenarios in MAN, we consider the transmitter- and the receiver-oriented node association rules as well as the air-to-ground and air-to-air channel models, which form line of sight links with a location-dependent probability. We then newly analyze the association probability, the main link distance distribution, successful transmission probability (STP), and area spectral efficiency (ASE) of MAN. The upper bounds of the optimal densities that maximize STP and ASE are also provided. Finally, in the numerical results, we show the optimal UAV densities of each AN that maximize the ASE and the STP decrease with the altitude of the network. We also show that when the total UAV density is fixed for two layer AN, the use of single layer in higher(lower) altitude only for all UAVs can achieve better performance for low(high) total density case. Otherwise, distributing UAVs in two layers, i.e., MAN, achieves better performance.
引用
收藏
页码:325 / 339
页数:15
相关论文
共 50 条
[21]   Wireless Powered Coniniunication Networks Aided by an Unmanned Aerial Vehicle [J].
Park, Junhee ;
Lee, Hoon ;
Eom, Subin ;
Lee, Inkyu .
2018 IEEE 88TH VEHICULAR TECHNOLOGY CONFERENCE (VTC-FALL), 2018,
[22]   A Tunable Caching Distribution Model for Unmanned Aerial Vehicle Networks [J].
Tang, Wenfei ;
Zhang, Hongtao .
IEEE INTERNET OF THINGS JOURNAL, 2022, 9 (11) :8646-8656
[23]   Analysis of the Urban Air Mobility for the Unmanned Aerial Vehicle [J].
Yena, Maksym ;
Bykov, Andriy ;
Karatanov, Oleksandr .
INTEGRATED COMPUTER TECHNOLOGIES IN MECHANICAL ENGINEERING-2023, VOL 1, ICTM 2023, 2024, 1008 :66-74
[24]   A tree-based data collection protocol for optical unmanned aerial vehicle networks [J].
Ramdhan, Nihel ;
Sliti, Maha ;
Boudriga, Noureddine .
COMPUTERS & ELECTRICAL ENGINEERING, 2018, 66 :80-97
[25]   Development of an Unmanned Aerial Vehicle for the Measurement of Turbulence in the Atmospheric Boundary Layer [J].
Witte, Brandon M. ;
Singler, Robert F. ;
Bailey, Sean C. C. .
ATMOSPHERE, 2017, 8 (10)
[26]   Improvement of the Control System for the Wing Performance of an Unmanned Aerial Vehicle [J].
Moiseev I.A. ;
Osintsev K.V. ;
Kuskarbekova S.I. ;
Ershov A.A. .
Russian Aeronautics, 2023, 66 (03) :485-492
[27]   Latency and energy-efficient multi-hop routing protocol for unmanned aerial vehicle networks [J].
Ateya, Abdelhamied A. ;
Muthanna, Ammar ;
Gudkova, Irina ;
Gaidamaka, Yuliya ;
Algarni, Abeer D. .
INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS, 2019, 15 (08)
[28]   Research on Coverage and Handover Performance of Unmanned Aerial Vehicle Network [J].
Jiao M.-H. ;
Peng M.-G. ;
Liu C.-X. .
Beijing Youdian Daxue Xuebao/Journal of Beijing University of Posts and Telecommunications, 2020, 43 (06) :74-81
[29]   Lightweight multi-target detection algorithm for unmanned aerial vehicle aerial imagery [J].
Liu, Yang ;
Ma, Ding ;
Wang, Yongfu .
JOURNAL OF APPLIED REMOTE SENSING, 2023, 17 (04)
[30]   Multi-Layer Airborne FSO Systems: Performance Analysis and Optimization [J].
Elamassie, Mohammed ;
Uysal, Murat .
IEEE TRANSACTIONS ON COMMUNICATIONS, 2025, 73 (04) :2522-2537