In massive multiple-input multiple-output (MIMO) system, channel state information (CSI) is essential for the base station (BS) to achieve high performance gain. The CSI matrix needs to be estimated and fed back from user equipment (UE) in frequency division duplexing (FDD) mode. Recently, deep learning is widely used in CSI compression to reduce the feedback overhead. However, applying neural network brings extra memory and computation cost, which is non-negligible especially for the resource limited UE. In this letter, a novel binarization aided feedback network named BCsiNet is introduced to lighten the encoder at UE. The proposed BCsiNet offers over $30\times $ memory saving and around $2\times $ inference acceleration for the encoder compared with CsiNet. Moreover, experiments show that the feedback performance of BCsiNet can be comparable with original CsiNet even with the encoder binarized.

中文翻译：

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大规模MIMO系统中二元神经网络辅助CSI反馈

在大规模多输入多输出 (MIMO) 系统中，信道状态信息 (CSI) 对于基站 (BS) 实现高性能增益至关重要。CSI矩阵需要在频分双工（FDD）模式下从用户设备（UE）进行估计和反馈。最近，深度学习被广泛用于 CSI 压缩以减少反馈开销。然而，应用神经网络会带来额外的内存和计算成本，尤其对于资源有限的 UE 而言，这是不可忽略的。在这封信中，引入了一种名为 BCsiNet 的新型二值化辅助反馈网络，以减轻 UE 的编码器。提议的 BCsiNet 提供超过 $30\times $ 节省内存和周围 $2\times $ 与 CsiNet 相比，编码器的推理加速。此外，实验表明，即使编码器二值化，BCsiNet 的反馈性能也可以与原始 CsiNet 相媲美。