  As an emerging approach, deep learning plays an increasingly influential role
in channel modeling. Traditional ray tracing (RT) methods of channel modeling
tend to be inefficient and expensive. In this paper, we present a
super-resolution (SR) model for channel characteristics. Residual connection
and attention mechanism are applied to this convolutional neural network (CNN)
model. Experiments prove that the proposed model can reduce the noise
interference generated in the SR process and solve the problem of low
efficiency of RT. The mean absolute error of our channel SR model on the PL
achieves the effect of 2.82 dB with scale factor 2, the same accuracy as RT
took only 52\% of the time in theory. Compared with vision transformer (ViT),
the proposed model also demonstrates less running time and computing cost in SR
of channel characteristics.
