Article (Scientific journals)
Channel Estimation for UAV Communication Systems Using Deep Neural Networks
Al-Gburi, Ahmed; Abdullah, Osamah; Sarhan, Akram et al.
2022In Drones
Peer reviewed
 

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Keywords :
channel modeling; deep learning; unmanned aerial vehicles (UAVs)
Abstract :
[en] Channel modeling of unmanned aerial vehicles (UAVs) from wireless communications has gained great interest for rapid deployment in wireless communication. The UAV channel has its own distinctive characteristics compared to satellite and cellular networks. Many proposed techniques consider and formulate the channel modeling of UAVs as a classification problem, where the key is to extract the discriminative features of the UAV wireless signal. For this issue, we propose a framework of multiple Gaussian–Bernoulli restricted Boltzmann machines (GBRBM) for dimension reduction and pre-training utilization incorporated with an autoencoder-based deep neural network. The developed system used UAV measurements of a town’s already existing commercial cellular network for training and validation. To evaluate the proposed approach, we run ray-tracing simulations in the program Remcom Wireless InSite at a distinct frequency of 28 GHz and used them for training and validation. The results demonstrate that the proposed method is accurate in channel acquisition for various UAV flying scenarios and outperforms the conventional DNNs.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Al-Gburi, Ahmed
Abdullah, Osamah
Sarhan, Akram
Al-Hraishawi, Hayder  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
External co-authors :
yes
Language :
English
Title :
Channel Estimation for UAV Communication Systems Using Deep Neural Networks
Publication date :
2022
Journal title :
Drones
Peer reviewed :
Peer reviewed
Focus Area :
Security, Reliability and Trust
Available on ORBilu :
since 14 December 2022

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