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In recent years, mobile communication technology is developing rapidly, and people's demand for wireless high-speed data communication is increasing day by day. In order to meet the needs of people, some key techniques to enhance the spectrum and energy efficiency are proposed, among which orthogonal frequency division multiplexing (OFDM) technology is a modulation method widely used in wireless broadband systems to combat frequency selective fading in wireless channels. However, the use of higher-order modulation makes the system complex, and more advanced channel estimation techniques are needed to recover the original signal more efficiently at the receiver side. In this paper, the channel transmission matrix is treated as a natural image processing and a deep CNN based denoising network is trained. The network used in this paper has the following advantages: (1) Improving the learning ability of the denoising network by increasing the width instead of the depth. (2) Using the null convolution to expand the perceptual field enables the network to extract more contextual information and reduce the computational cost. (3) Solving the mini-batch problem under hardware resource constrained conditions by Batch renormalization. Also it can accelerate the convergence of the network training. We simulate the OFDM based communication system, and the results prove that the method has excellent performance.
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