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.

中文翻译：

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用于大规模 MIMO 系统的基于扩张卷积的 CSI 反馈压缩

尽管频分双工 (FDD) 大规模多输入多输出 (MIMO) 系统可以提供高频谱和能量效率，但它需要从用户向基站 (BS) 反馈下行链路信道状态信息 (CSI)，以完成基站的预编码设计。然而，大规模 MIMO 系统中 CSI 矩阵的大维数使得 CSI 反馈非常具有挑战性，迫切需要对反馈 CSI 进行压缩。为此，本文提出了一种新颖的基于空洞卷积的 CSI 反馈网络，即 DCRNet。具体来说，扩张的卷积用于增强所提出的 DCRNet 的感受野（RF）而不增加卷积大小。此外，高级编码器和解码器块旨在提高重建性能并降低计算复杂度。数值结果显示了所提出的 DCRNet 相对于传统网络的优越性。特别是，所提出的 DCRNet 可以以低得多的浮点运算 (FLOP) 实现几乎最先进的 (SOTA) 性能。这项工作的开源代码和检查点可在 https://github.com/recusant7/DCRNet 获得。