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

) complexity, and existing deep learning approaches treat pilot design and channel estimation independently. This paper proposes a joint algorithm based on a convolutional block attention module-enhanced convolutional neural network (CBAM-CNN), in which a differentiable Concrete selector layer and a CBAM-CNN estimation network share a unified loss function for end-to-end co-optimization. The channel attention sub-module captures inter-feature correlations while the spatial attention sub-module exploits inter-subcarrier correlations along the frequency axis to restore channel continuity disrupted by multipath scattering. Simulations under a Nakagami fading model with m = 0.85 demonstrate that the proposed method achieves a normalized mean square error of 0.0089 at SNR = 10 dB, reducing estimation error by 87.3% relative to LS and 41.1% relative to FSRCNN, while approaching MMSE accuracy at O(N) complexity. Decoupled ablation experiments were conducted to determine the impact of channel attention, spatial attention, skip connection, and differential pilot selection on model performance. The full combination achieves a 48.0% improvement in NMSE compared to the baseline CNN and outperforms the linear combination, which achieves only 33.3%, demonstrating a synergistic relationship between the learned pilot pattern and the attention module. The algorithm exhibits robust behavior under various pilot densities, fading characteristics, multipath delay spreads, and SNR mismatch scenarios, allowing for a maximum pilot overhead saving of 50% in the simulated Nakagami channel. The practical test of the algorithm in mine tunnels is recommended for future work.