针对煤矿井下环境恶劣，传统信道估计算法存在准确度低的问题，提出一种改进图像超分辨卷积网络（Super Resolution Convolutional Network, SRCNN）进行信道估计。在改进SRCNN模型中，将导频处的估计值作为输入，改进SRCNN模型取代了传统信道估计算法中的插值过程，降低了复杂度，并加入注意力机制ECA模块提高通道特征的学习，实现对煤矿井下环境更准确的信道估计。仿真结果表明：改进SRCNN模型的信道估计算法优于传统的信道估计算法，与SRCNN模型的信道估计相比，其估计精度提升了1个数量级。

Aiming at the problem of low accuracy of traditional channel estimation algorithms in the harsh environment of underground coal mines, this paper proposes an improved Super Resolution Convdutional Network (SRCNN) for channel estimation. In the improved SRCNN model, the estimated value at the pilot frequency is used as input, and the improved SRCNN model replaces the interpolation process in the traditional channel estimation algorithm to reduce the complexity, and the attention mechanism ECA module is added to improve the learning of channel features to achieve more accurate channel estimation for the underground coal mine environment. Simulation results show that the channel estimation algorithm of the improved SRCNN model outperforms the traditional channel estimation algorithm and improves the estimation accuracy by one order of magnitude compared with the channel estimation of the SRCNN model.