Abstract
As the key to wireless communication, channel estimation has become a hot research topic in recent years. In this paper, we propose a deep learning method based on the channel estimation of inverse convolutional network and expanded convolutional network to address the problems that the performance of traditional channel estimation algorithms in orthogonal frequency division multiplexing (OFDM) systems can hardly meet the communication requirements of complex scenarios and are greatly affected by noise. The method constructs a lightweight deconvolutional network using the correlation of channels, and achieves channel interpolation and estimation step by step with a few layers of deconvolutional operations, which achieves channel estimation with low complexity. To improve the estimation performance, an expanded convolutional network is further constructed to suppress the channel noise and improve the accuracy of channel estimation. The simulation results show that the channel estimation can be performed at different signal levels. The simulation results show that the proposed deep learning method based on deconvolution and dilation convolution has lower estimation error and lower complexity than the traditional methods under different signal-to-noise ratios (SNR).
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