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RETRACTED: Ndaguba et al. Operability of Smart Spaces in Urban Environments: A Systematic Review on Enhancing Functionality and User Experience. <i>Sensors</i> 2023, <i>23</i>, 6938.

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Updated: May 5, 2026

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
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A Novel Two-Step Channel Estimation Method for RIS-Assisted mmWave Systems.

Jiarun Yu1

  • 1School of Information and Communications Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Sensors (Basel, Switzerland)
|August 29, 2024
PubMed
Summary

This study introduces a novel two-step method using MMPGA-LS and ResNet for cascaded channel estimation and MTRnet for reflected channel estimation in reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) systems, significantly reducing estimation errors.

Keywords:
MMPGAMTRnetRIS-assisted mmWave systemsResNetchannel estimationmulti-task regression model

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Area of Science:

  • Wireless Communications
  • Signal Processing
  • Artificial Intelligence

Background:

  • Reconfigurable intelligent surfaces (RIS) offer enhanced control over wireless environments.
  • Millimeter-wave (mmWave) systems face challenges in channel estimation due to complex propagation.
  • Accurate channel estimation is crucial for efficient RIS-assisted mmWave communication.

Purpose of the Study:

  • To resolve the cascaded and reflected channel estimation problems in RIS-assisted mmWave systems.
  • To develop a novel two-step method combining optimization algorithms and neural networks.
  • To improve the accuracy and efficiency of channel estimation in these systems.

Main Methods:

  • A modified multiple population genetic algorithm (MMPGA) optimized crossover and mutation strategies.
  • A residual network (ResNet) for cascaded channel estimation using MMPGA-LS output.
  • A multi-task ResNet (MTRnet) for estimating reflected channels and reconstructing UE-RIS and RIS-BS channels.

Main Results:

  • The proposed MMPGA-LS optimizes genetic algorithm strategies for better performance.
  • ResNet effectively learns the relationship for cascaded channel estimation.
  • MTRnet demonstrates superior performance in estimating multiple reflected channels with lower norm mean square error (NMSE).

Conclusions:

  • The developed two-step method significantly improves channel estimation accuracy in RIS-assisted mmWave systems.
  • MTRnet offers a more efficient approach for multi-channel estimation compared to traditional methods.
  • The method shows effectiveness even with uniform planar array RIS configurations.