Journal
IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION
Volume 67, Issue 6, Pages 4022-4031Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAP.2019.2905665
Keywords
Artificial neural network (ANN); cellular communications; differential evolution (DE); optimization methods; unmanned aerial vehicle (UAV)
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available