Multi-resolution CSI Feedback with deep learning in Massive MIMO System
This work addresses the feedback efficiency problem for massive MIMO communication systems, representing an incremental improvement over existing deep learning methods.
The paper tackles the high feedback cost of channel state information in massive MIMO systems by proposing CRNet, a deep learning-based feedback network that extracts multi-resolution features, achieving better performance than the state-of-the-art CsiNet under the same computational complexity.
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