Synthesize multiple V/H directional beams for high altitude platform station based on deep-learning algorithm

• Published in: Scientific reports Vol. 15; no. 1; pp. 10846 - 22
• Main Authors: Mahmoud, Korany R., Montaser, Ahmed M.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 29.03.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166; 639/166/987; 639/925/930/1032; Algorithms; Altitude; Antenna array; Antennas; Beamforming technique; Communications networks; Computer simulation; Connectivity; Cost control; Deep learning; Deep neural network; Efficiency; Energy storage; Gravity; High-altitude environments; Humanities and Social Sciences; Machine learning; multidisciplinary; Neural networks; Optimization; Optimization algorithm; Radiation; Radio communications; Regulatory reform; Rural areas; Satellites; Science; Science (multidisciplinary); Surveillance; Unmanned aerial vehicles; Deep learning; Deep neural network; Optimization algorithm; Beamforming technique; Antenna array
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-93251-7

This paper investigates the integration of High-Altitude Platform Stations (HAPS) with Deep Learning (DL) models to enhance coverage capabilities. Recognizing the inherent limitations of traditional HAPS coverage, which is typically confined to a circular area, this work proposes a novel approach utilizing a 60-element Concentric Circular Array (CCA) operating at 2.1 GHz. To dynamically generate multiple vertical/horizontal (V/H) directional beams, the system integrates a Deep Neural Network (DNN) with a modified version of the Gravitational Search Algorithm and Particle Swarm Optimization (MGSA-PSO) algorithm. This hybrid approach optimizes the feeding phases of the CCA elements, enabling the system to effectively cover diverse road paths. Furthermore, the study incorporates realistic scenarios by utilizing the Computer Simulation Technology-Microwave Studio Suite (CST) with the Earth Explorer (EE) user interface tool to model real-world road paths, including those traversing challenging terrains such as rugged deserts with mountain chains and forested areas.