Although the frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) system can offer high spectral and energy efficiency, it requires to feedback the downlink channel state information (CSI) from users to the base station (BS), in order to fulfill the precoding design at the BS. However, the large dimension of CSI matrices in the massive MIMO system makes the CSI feedback very challenging, and it is urgent to compress the feedback CSI. To this end, this paper proposes a novel dilated convolution based CSI feedback network, namely DCRNet. Specifically, the dilated convolutions are used to enhance the receptive field (RF) of the proposed DCRNet without increasing the convolution size. Moreover, advanced encoder and decoder blocks are designed to improve the reconstruction performance and reduce computational complexity as well. Numerical results are presented to show the superiority of the proposed DCRNet over the conventional networks. In particular, the proposed DCRNet can achieve almost the state-of-the-arts (SOTA) performance with much lower floating point operations (FLOPs). The open source code and checkpoint of this work are available at https://github.com/recusant7/DCRNet.

翻译：虽然频谱分解(DFD)大规模多投入多产出(MIMO)系统可以提供高光谱和高能效,但它需要用户向基地站反馈下链频道国家信息(CSI),以便完成BS的预编码设计。然而,大型MIMO系统中的CSI矩阵规模很大,使得CSI反馈极具挑战性,因此迫切需要压缩CSI反馈。为此,本文件提议建立一个基于CSI的新的扩展式共变反馈网络,即DCRNet。具体地说,扩展式连接用于在不增加连动规模的情况下加强拟议的DCRNet的接收场。此外,高级编码和解码块的设计是为了改进重建性能和降低计算复杂性。提供数字结果,以显示拟议的DCRNet在常规网络中的优势。特别是,拟议的DCRNet可以几乎实现国家-艺术(SOCRNet)的状态代码(SODA)的功能,而该功能在远低浮点/DRMRUPS。