  The reconfigurable intelligent surface (RIS) is considered as a promising new
technology for reconfiguring wireless communication environments. To acquire
the channel information accurately and efficiently, we only turn on a fraction
of all the RIS elements, formulate a sub-sampled RIS channel, and design a deep
learning based scheme to extrapolate the full channel information from the
partial one. Specifically, inspired by the ordinary differential equation
(ODE), we set up connections between different data layers in a convolutional
neural network (CNN) and improve its structure. Simulation results are provided
to demonstrate that our proposed ODE-based CNN structure can achieve faster
convergence speed and better solution than the cascaded CNN.
