A Two-Step Learning and Interpolation Method for Location-Based Channel Database
This work addresses the need for pilot-free channel estimation in wireless networks, but it is incremental as it builds on existing interpolation techniques with a hybrid approach.
The paper tackles the problem of incomplete channel state information coverage in cell sleeping scenarios by proposing a two-step interpolation method that combines K-nearest-neighbor and deep convolutional neural networks to infer channels at uncovered locations, showing a great performance advantage over conventional methods when applied to ray tracing data.
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.