  Timely and accurate knowledge of channel state information (CSI) is necessary
to support scheduling operations at both physical and network layers. In order
to support pilot-free channel estimation in cell sleeping scenarios, we propose
to adopt a channel database that stores the CSI as a function of geographic
locations. Such a channel database is generated from historical user records,
which usually can not cover all the locations in the cell. Therefore, we
develop a two-step interpolation method to infer the channels at the uncovered
locations. The method firstly applies the K-nearest-neighbor method to form a
coarse database and then refines it with a deep convolutional neural network.
When applied to the channel data generated by ray tracing software, our method
shows a great advantage in performance over the conventional interpolation
methods.
