Visible Channel Estimation in Coal Mine Based on RSA-SAMP Algorithm

In underground coal mine visible light communication (VLC) systems, channel estimation is crucial for reliable data transmission. Traditional algorithms, such as Sparse Adaptive Matching Pursuit (SAMP), suffer from high computational complexity and slow convergence, making them unsuitable for meeting the communication needs in harsh underground environments. To address these issues, this paper proposes a Residual - Statistics - driven Sparse Adaptive Matching Pursuit (RSA - SAMP) algorithm. It introduces residual statistics analysis, dynamically adjusts the step size, and optimizes the termination condition. This enhances estimation accuracy and convergence speed, enabling efficient and precise channel estimation without prior knowledge of channel sparsity. Simulation results show that RSA - SAMP effectively boosts the noise - robustness and estimation accuracy of coal mine VLC systems, offering theoretical and technical insights for reliable communication in harsh conditions.