  Research on machine learning for channel estimation, especially neural
network solutions for wireless communications, is attracting significant
current interest. This is because conventional methods cannot meet the present
demands of the high speed communication. In the paper, we deploy a general
residual convolutional neural network to achieve channel estimation for the
orthogonal frequency-division multiplexing (OFDM) signals in a downlink
scenario. Our method also deploys a simple interpolation layer to replace the
transposed convolutional layer used in other networks to reduce the computation
cost. The proposed method is more easily adapted to different pilot patterns
and packet sizes. Compared with other deep learning methods for channel
estimation, our results for 3GPP channel models suggest improved mean squared
error performance for our approach.
