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
翻译:在大型多投入多重产出系统(MIMO)中,用户设备(UE)需要向下链接频道状态信息(CSI)返回基地站(BS),然而,随着大型MIMO系统CSI日益复杂,反馈变得昂贵。最近,利用深层次的学习(DL)方法来提高CSI反馈的重建效率。在本文中,提议建立一个名为CRNet的新颖反馈网络,通过在多个分辨率上提取 CSI 特征来取得更好的业绩。还引入了进一步提升网络性能的高级培训计划。模拟结果表明,拟议的CRNet在没有额外信息的情况下,在相同的计算复杂度下超过了最新的CsiNet。开放源代码可在https://github.com/Kylin9511/CRNet上查阅。