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

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
PubMed
Summary
This summary is machine-generated.

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This study enhances High-Altitude Platform Station (HAPS) coverage using deep learning and a specialized antenna array. The novel approach optimizes beamforming for dynamic, wide-area network coverage, overcoming traditional limitations.

Area of Science:

  • Wireless communication
  • Antenna array design
  • Machine learning applications

Background:

  • Traditional High-Altitude Platform Station (HAPS) coverage is limited to a fixed circular area.
  • There is a need for dynamic beamforming to adapt to diverse geographical terrains and user demands.
  • Optimizing antenna element feeding phases is crucial for flexible coverage patterns.

Purpose of the Study:

  • To investigate the integration of HAPS with Deep Learning (DL) for enhanced coverage.
  • To propose a novel approach using a 60-element Concentric Circular Array (CCA) for dynamic beam generation.
  • To enable effective coverage of diverse and challenging road paths.

Main Methods:

  • Utilizing a 60-element Concentric Circular Array (CCA) at 2.1 GHz.
Keywords:
Antenna arrayBeamforming techniqueDeep learningDeep neural networkOptimization algorithm

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  • Integrating a Deep Neural Network (DNN) with a modified Gravitational Search Algorithm and Particle Swarm Optimization (MGSA-PSO).
  • Employing Computer Simulation Technology-Microwave Studio Suite (CST) and Earth Explorer (EE) for realistic scenario modeling.

    • Main Results:

    • The hybrid DNN-MGSA-PSO approach successfully optimizes CCA feeding phases.
    • Dynamic generation of multiple vertical/horizontal (V/H) directional beams was achieved.
    • Effective coverage of complex road paths in varied terrains was demonstrated through simulations.

    Conclusions:

    • The proposed HAPS-DL integration offers a significant advancement in wireless coverage capabilities.
    • The optimized beamforming strategy effectively addresses the limitations of traditional HAPS.
    • This technology holds promise for robust communication in diverse and challenging environments.