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Related Experiment Videos

A joint pilot optimization and channel estimation algorithm based on CBAM-CNN for multipath fading environments in

Fei Han1, Chaoyang Liu2, Yan Lei1

  • 1College of Information and Business, Shaanxi Energy Institute, Xianyang, 712000, China.

Scientific Reports
|June 18, 2026
PubMed
Summary

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This study introduces a novel deep learning algorithm for accurate channel estimation in underground coal mine communication systems. The method significantly reduces estimation errors, improving reliability and efficiency in challenging environments.

Area of Science:

  • Electrical Engineering
  • Wireless Communications
  • Signal Processing

Background:

  • Underground coal mine communications face severe multipath fading, complicating accurate channel estimation.
  • Existing methods like Least Squares (LS) and Minimum Mean Square Error (MMSE) have limitations in accuracy, complexity, or independent pilot design.
  • Deep learning approaches often fail to jointly optimize pilot design and channel estimation.

Purpose of the Study:

  • To propose a novel joint algorithm for accurate channel estimation in underground coal mine Orthogonal Frequency Division Multiplexing (OFDM) systems.
  • To enhance channel estimation by co-optimizing pilot design and a Convolutional Block Attention Module-enhanced Convolutional Neural Network (CBAM-CNN).
  • To address the challenges of multipath fading, noise sensitivity, and high complexity in existing estimation techniques.
Keywords:
Channel estimationConvolutional neural networkOFDMPilot optimizationUnderground coal mine

Related Experiment Videos

Main Methods:

  • Developed a joint algorithm using a CBAM-CNN with a differentiable Concrete selector layer for end-to-end co-optimization.
  • Employed channel attention to capture inter-feature correlations and spatial attention to exploit inter-subcarrier correlations for channel continuity.
  • Utilized a unified loss function for the pilot selection and estimation network.

Main Results:

  • Achieved a Normalized Mean Square Error (NMSE) of 0.0089 at 10 dB Signal-to-Noise Ratio (SNR), outperforming LS by 87.3% and FSRCNN by 41.1%.
  • Demonstrated MMSE-level accuracy with significantly reduced O(N) complexity.
  • Showcased robust performance across various channel conditions, pilot densities, and SNR mismatches, enabling up to 50% pilot overhead reduction.

Conclusions:

  • The proposed CBAM-CNN based joint algorithm effectively enhances channel estimation accuracy and efficiency in underground coal mine OFDM systems.
  • The synergistic co-optimization of pilot design and channel estimation, powered by attention mechanisms, significantly outperforms baseline methods.
  • The algorithm offers a practical and robust solution for reliable wireless communication in challenging subterranean environments.