Scholarlink

<p>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&#xa0;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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月29日

查看全文
  • Korany R. Mahmoud,
  • Ahmed M. Montaser

摘要

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.