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Optimized Classification of Intelligent Reflecting Surface (IRS)-Enabled GEO Satellite Signals.

Mamoona Jamil1, Mubashar Sarfraz2, Sajjad A Ghauri1

  • 1School of Engineering & Applied Sciences, ISRA University, Islamabad 46000, Pakistan.

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|April 28, 2023
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Summary
This summary is machine-generated.

Intelligent Reflecting Surfaces (IRS) enhance GEO satellite communication by improving channel conditions. This IRS-based method boosts classification accuracy compared to traditional approaches.

Keywords:
GEO satellite signalsGabor filter networkglobal and local search methodsintelligent reflecting surfacemodulation classification

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Area of Science:

  • Wireless communication engineering
  • Signal processing
  • Metamaterials science

Background:

  • Intelligent Reflecting Surfaces (IRS) offer a novel approach to enhance wireless communication efficiency.
  • IRS technology enables passive beamforming without Radio-Frequency (RF) chains, improving spectrum and energy efficiency.
  • IRS can significantly improve wireless channel conditions and communication system dependability.

Purpose of the Study:

  • To propose and evaluate a scheme for an IRS-equipped Geostationary Earth Orbit (GEO) satellite signal.
  • To investigate the use of Gabor Filter Networks (GFNs) for feature extraction and classification in IRS-enhanced systems.
  • To demonstrate the performance improvement of an IRS-based methodology over a benchmark system.

Main Methods:

  • Development of a proper channel model and system characterization for IRS-equipped GEO satellite signals.
  • Joint proposal of Gabor Filter Networks (GFNs) for distinct feature extraction and classification.
  • Utilization of hybrid optimal functions to address the classification problem.
  • Design of a simulation setup with appropriate channel modeling.

Main Results:

  • The proposed IRS-based methodology demonstrated superior performance in classification accuracy.
  • Experimental results confirmed the effectiveness of the IRS in improving wireless channel conditions.
  • The GFNs effectively extracted and classified features for the IRS-equipped system.

Conclusions:

  • The IRS-equipped GEO satellite scheme offers a significant advancement in wireless communication.
  • The proposed methodology, integrating IRS, GFNs, and hybrid optimal functions, enhances classification accuracy.
  • This approach provides a cost-effective solution for future spectrum- and energy-efficient wireless systems.