Multi-resolution CSI Feedback with deep learning in Massive MIMO System
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Publication:6328246
arXiv1910.14322MaRDI QIDQ6328246
Author name not available (Why is that?)
Publication date: 31 October 2019
Abstract: 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
Has companion code repository: https://github.com/Kylin9511/CRNet
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