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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
Summary
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.
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.
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