scholarly journals Downlink Indoor Coverage Performance of Unmanned Aerial Vehicle LTE Base Stations

2021 ◽  
Vol 6 (1) ◽  
pp. 128-133
Author(s):  
Mahmut Demirtas ◽  
Kerem C¸ a˘gdas¸ Durmus¸ ◽  
G¨ulc¸in Tanıs ◽  
Caner Arslan ◽  
Metin Balcı
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Base Stations ◽  
Indoor Coverage ◽  
Aerial Vehicle

scholarly journals Deep learning–based fifth-generation millimeter-wave communication channel tracking for unmanned aerial vehicle Internet of things networks

2019 ◽  
Vol 15 (8) ◽  
pp. 155014771986588 ◽  
Author(s):  
Shan Meng ◽  
Xin Dai ◽  
Bicheng Xiao ◽  
Yimin Zhou ◽  
Yumei Li ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Unmanned Aerial Vehicle ◽  
Millimeter Wave ◽  
Estimation Method ◽  
Attitude Estimation ◽  
Base Stations ◽  
Communication Link ◽  
Multiple Sensors ◽  
Channel Tracking ◽  
Aerial Vehicle

Using unmanned aerial vehicle as movable base stations is a promising approach to enhance network coverage. Moreover, movable unmanned aerial vehicle–base stations can dynamically move to the target devices to expand the communication range as relays in the scenario of the Internet of things. In this article, we consider a communication system with movable unmanned aerial vehicle–base stations in millimeter-Wave. The movable unmanned aerial vehicle–base stations are equipped with antennas and multiple sensors for channel tracking. The cylindrical array antenna is mounted on the movable unmanned aerial vehicle–movable base stations, making the beam omnidirectional. Furthermore, the attitude estimation method using the deep neural network can replace the traditional attitude estimation method. The estimated unmanned aerial vehicle attitude information is combined with beamforming technology to realize a reliable communication link. Simulation experiments have been performed, and the results have verified the effectiveness of the proposed method.

Placement Optimization of Unmanned Aerial Vehicle Base Stations for Maximal Coverage

2021 ◽  
Author(s):  
Yanzhi Hu ◽  
Fengbin Zhang ◽  
Tian Tian ◽  
Zhiyong Shi ◽  
Gang Yu ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Base Stations ◽  
Placement Optimization ◽  
Aerial Vehicle

scholarly journals Hierarchical Federated Learning for Edge-Aided Unmanned Aerial Vehicle Networks

2022 ◽  
Vol 12 (2) ◽  
pp. 670
Author(s):  
Jamshid Tursunboev ◽  
Yong-Sung Kang ◽  
Sung-Bum Huh ◽  
Dong-Woo Lim ◽  
Jae-Mo Kang ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Critical Issue ◽  
Heterogeneous Data ◽  
Base Stations ◽  
High Performing ◽  
Shared Data ◽  
Private Data ◽  
Machine Learning Model ◽  
Vehicle Networks ◽  
Aerial Vehicle

Federated learning (FL) allows UAVs to collaboratively train a globally shared machine learning model while locally preserving their private data. Recently, the FL in edge-aided unmanned aerial vehicle (UAV) networks has drawn an upsurge of research interest due to a bursting increase in heterogeneous data acquired by UAVs and the need to build the global model with privacy; however, a critical issue is how to deal with the non-independent and identically distributed (non-i.i.d.) nature of heterogeneous data while ensuring the convergence of learning. To effectively address this challenging issue, this paper proposes a novel and high-performing FL scheme, namely, the hierarchical FL algorithm, for the edge-aided UAV network, which exploits the edge servers located in base stations as intermediate aggregators with employing commonly shared data. Experiment results demonstrate that the proposed hierarchical FL algorithm outperforms several baseline FL algorithms and exhibits better convergence behavior.

scholarly journals Positioning of Multiple Unmanned Aerial Vehicle Base Stations in Future Wireless Network

2020 ◽  
Author(s):  
Thushan Sivalingam ◽  
K. B. Shashika Manosha ◽  
Nandana Rajatheva ◽  
M. Latva-aho ◽  
Maheshi B. Dissanayake
Keyword(s):  
Wireless Network ◽  
Unmanned Aerial Vehicle ◽  
Base Stations ◽  
Aerial Vehicle
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