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Title: Artificial Neural Network Optimal Modeling and Optimization of UAV Measurements for Mobile Communications Using the L-SHADE Algorithm
Authors: Goudos, Sotirios K.
Tsoulos, George V.
Athanasiadou, Georgia
Batistatos, Michael C.
Zarbouti, Dimitra
Psannis, Kostas E.
Type: Article
Subjects: FRASCATI::Engineering and technology
Keywords: Artificial neural network (ANN)
cellular communications
differential evolution (DE)
differential evolution (DE)
optimization methods
unmanned aerial vehicle (UAV)
Antenna measurements
Long Term Evolution
Power measurement
Area measurement
Frequency measurement
evolutionary algorithms
Issue Date: Jun-2019
Source: IEEE Transactions on Antennas and Propagation
Volume: 67
Issue: 6
First Page: 4022
Last Page: 4031
Abstract: Channel modeling of wireless communications from unmanned aerial vehicles (UAVs) is an emerging research challenge. In this paper, we propose a solution to this issue by applying a new framework for the prediction of received signal strength (RSS) in mobile communications based on artificial neural networks (ANNs). The experimental data measurements are taken with a UAV at different altitudes. We apply several evolutionary algorithms (EAs) in conjunction with the Levenberg-Marquardt (LM) backpropagation algorithm in order to train different ANNs and in particular the L-SHADE algorithm, which self-adapts control parameters and dynamically adjusts population size. Five new hybrid training methods are designed by combining LM with self-adaptive differential evolution (DE) strategies. These new training methods obtain better performance to ANN weight optimization than the original LM method. The received results are compared with the real values using representative ANN performance indices and exhibit satisfactory accuracy.
ISSN: 0018-926X
Other Identifiers: 10.1109/TAP.2019.2905665
Appears in Collections:Department of Applied Informatics

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