Artificial Neural Network Optimal Modeling and Optimization of UAV Measurements for Mobile Communications Using the L-SHADE Algorithm
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.
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With the advent of AI and the deployment of more complex models such as neural networks, CAs offer the potential to be sophisticated interactive systems, assisting customers to handle complaints and acting as learning assistants in tutoring students or employees.
“With the advent of artificial intelligence (AI) and the deployment of more complex models such as neural networks (Sedik et al., 2022;Goudos et al., 2019;Kushwaha & Kar, 2021), CAs offer the potential to be sophisticated interactive systems, assisting not only customers to handle their complaints but also acting as learning assistants in tutoring students or employees, for instance (Schmitt et al.”
From Charting the Evolution and Future of Conversational Agents: A Research Agenda Along Five Waves and New Frontiers (2024) · cites this paper as Goudos et al., 2019
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- Authors
- Goudos, S. K., Tsoulos, G. V., Athanasiadou, G., Batistatos, M. C., Zarbouti, D., & Psannis, K. E.
- Affiliations
- Aristotle University of Thessaloniki (GR)
- University of Peloponnese (GR)
- University of Macedonia (GR)
- ORCID
- Venue
- IEEE Transactions on Antennas and Propagation
- Published
- 2019-06
- Volume
- 67
- Issue
- 6
- Pages
- 4022-4031
- ISSN
- 0018-926X (ISSN-L)
- 1558-2221
- OpenAlex
- W2921100919
- Cited by
- 50
- References
- 26
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 7 · 2023: 8 · 2022: 6 · 2021: 14 · 2020: 4 · 2019: 5
- FWCI
- 5.33
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Antennas and Propagation (publishedVersion)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › UAV Applications and Optimization
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Physical Sciences › Engineering › Aerospace Engineering › Aerospace and Aviation Technology
- Keywords
- received signal strength prediction, artificial neural networks, L-SHADE, self-adaptive differential evolution, Levenberg-Marquardt backpropagation, hybrid neural network training, mobile communication, evolutionary algorithms
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