  In massive multiple-input multiple-output (MIMO) system, user equipment (UE)
needs to send downlink channel state information (CSI) back to base station
(BS). However, the feedback becomes expensive with the growing complexity of
CSI in massive MIMO system. Recently, deep learning (DL) approaches are used to
improve the reconstruction efficiency of CSI feedback. In this paper, a novel
feedback network named CRNet is proposed to achieve better performance via
extracting CSI features on multiple resolutions. An advanced training scheme
that further boosts the network performance is also introduced. Simulation
results show that the proposed CRNet outperforms the state-of-the-art CsiNet
under the same computational complexity without any extra information. The open
source codes are available at https://github.com/Kylin9511/CRNet
