相关实验视频
Updated: Jan 10, 2026
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Bacterial Phylum Spirochaetes
Published on: June 12, 2025
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基于深度学习算法,为高空平台站合成多个V/H定向光束
Korany R Mahmoud1,2, Ahmed M Montaser3
1Electronics and Communications Department, Faculty of Engineering, Helwan University, Cairo, 11795, Egypt. kurany_hameda@h-eng.helwan.edu.eg.
Scientific reports
|March 29, 2025
概括
这项研究通过使用深度学习和专门的天线阵列来增强高海拔平台站 (HAPS) 覆盖范围. 这种新的方法优化了光束成型,实现了动态的广域网络覆盖,克服了传统的局限性.
科学领域:
- 无线通信是一种无线通信.
- 天线阵列设计设计天线阵列设计
- 机器学习应用程序 机器学习应用程序
背景情况:
- 传统的高空平台站 (HAPS) 覆盖范围仅限于一个固定的圆形区域.
- 需要动态光束成型来适应不同的地理地形和用户需求.
- 优化天线元件料阶段对于灵活的覆盖模式至关重要.
研究的目的:
- 调查HAPS与深度学习 (DL) 的整合,以提高覆盖范围.
- 提出一种新的方法,使用60个元素的同心圆形阵列 (CCA) 进行动态光束生成.
- 为了能够有效地覆盖多样化和具有挑战性的道路路径.
主要方法:
- 使用一个以60元为中心的圆形阵列 (CCA),频率为2.1GHz.
- 集成深度神经网络 (DNN) 与修改后的引力搜索算法和粒子集群优化 (MGSA-PSO).
- 采用计算机模拟技术-微波工作室套件 (CST) 和地球探测器 (EE) 来实现现实的场景建模.
主要成果:
- 混合DNN-MGSA-PSO方法成功优化了CCA养阶段.
- 实现了多个垂直/水平 (V/H) 定向光束的动态生成.
- 通过模拟,通过各种地形证明了复杂道路路径的有效覆盖.
结论:
- 拟议的HAPS-DL集成为无线覆盖能力提供了显著的进步.
- 优化的束形策略有效地解决了传统HAPS的局限性.
- 这项技术有望在多样化和具有挑战性的环境中实现可靠的通信.
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