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

A Two-Step Learning and Interpolation Method for Location-Based Channel Database