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Channel estimation for reconfigurable intelligent surface-assisted mmWave based on Re'nyi entropy function.

Zaid Albataineh1, Khaled F Hayajneh2, Hazim Shakhatreh2

  • 1Department of Electronics Engineering, Yarmouk University, Irbid, 21163, Jordan. zaid.bataineh@yu.edu.jo.

Scientific Reports
|December 24, 2022
PubMed
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This summary is machine-generated.

This study introduces a novel channel estimation method for reconfigurable intelligent surface (RIS)-assisted mmWave systems. The technique minimizes pilot overhead and improves accuracy using compressive sensing, outperforming traditional methods.

Area of Science:

  • Wireless Communications
  • Signal Processing

Background:

  • Reconfigurable intelligent surfaces (RIS) are crucial for enhancing base-to-user data transfer in mmWave systems.
  • Accurate channel state information is essential for effective beamforming in these systems.
  • Traditional channel estimation methods face challenges with high pilot overhead in mmWave systems.

Purpose of the Study:

  • To develop an efficient channel estimation technique for RIS-assisted mmWave systems.
  • To reduce pilot overhead and computational complexity in channel estimation.
  • To improve the accuracy of channel estimation in downlink scenarios.

Main Methods:

  • Utilizing the inherent sparsity of mmWave channels for compressive sensing-based estimation.
  • Extending the Re'nyi entropy function as a sparsity-promoting regularizer.

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  • Employing Sparsity Adaptive Matching Pursuit (SAMP) to determine signal sparsity adaptively.

    • Main Results:

    • The proposed method significantly reduces pilot overhead compared to conventional techniques.
    • Achieved superior Normalized Mean Square Error (NMSE) performance over traditional OMP-based methods.
    • Demonstrated a substantial reduction in the computational cost of channel estimation.

    Conclusions:

    • The developed compressive sensing approach effectively addresses channel estimation challenges in RIS-assisted mmWave systems.
    • The method offers a practical solution for reducing training overhead while maintaining high estimation accuracy.
    • This technique enhances the efficiency and performance of future wireless communication systems.