论文标题
二进制神经网络协助CSI反馈大量MIMO系统
Binary Neural Network Aided CSI Feedback in Massive MIMO System
论文作者
论文摘要
在大量的多输入多输出（MIMO）系统中，通道状态信息（CSI）对于基站获得高性能增益至关重要。最近，深度学习被广泛用于CSI压缩中，以抵抗大量MIMO在频划分双链系统中带来的不断增长的反馈开销。但是，应用神经网络会带来额外的内存和计算成本，这是不可忽略的，特别是对于资源有限的用户设备（UE）而言。在本文中，引入了一个名为BCSinet的新型二进制辅助反馈网络。此外，BCSINET旨在提高定制培训和推理方案下的性能。实验表明，与CSINET相比，BCSINET提供了30美元以上的$ \ times $保存和大约2 $ \ times $ times $推理加速度。此外，BCSinet的反馈性能与原始CSINET相当。可以使用https://github.com/kylin9511/bcsinet复制关键结果。
In massive multiple-input multiple-output (MIMO) system, channel state information (CSI) is essential for the base station to achieve high performance gain. Recently, deep learning is widely used in CSI compression to fight against the growing feedback overhead brought by massive MIMO in frequency division duplexing system. However, applying neural network brings extra memory and computation cost, which is non-negligible especially for the resource limited user equipment (UE). In this paper, a novel binarization aided feedback network named BCsiNet is introduced. Moreover, BCsiNet variants are designed to boost the performance under customized training and inference schemes. Experiments shows that BCsiNet offers over 30$\times$ memory saving and around 2$\times$ inference acceleration for encoder at UE compared with CsiNet. Furthermore, the feedback performance of BCsiNet is comparable with original CsiNet. The key results can be reproduced with https://github.com/Kylin9511/BCsiNet.
