论文标题
大量MIMO CSI反馈的空间可分离注意机制
A Spatially Separable Attention Mechanism for massive MIMO CSI Feedback
论文作者
论文摘要
通道状态信息（CSI）反馈在通过波束形成获得更高的增长方面起着至关重要的作用。但是，对于大型的MIMO系统，此反馈开销是巨大的，并且随天线的数量线性增长。为了减少反馈开销，近年来实施了几种压缩感应（CS）技术，但是这些技术通常是迭代的，并且在计算上很复杂，无法在功率受限的用户设备（UE）中实现。因此，基于数据的深度学习方法接管了近年来，引入了各种CSI压缩的神经网络。具体而言，已经证明基于变压器的网络可以实现最新的性能。但是，多头注意操作是变形金刚的核心，是计算复杂的，使变压器难以在UE上实现。在这项工作中，我们提出了一个名为STNET的轻型变压器，该变压器使用了一种可分开的注意机制，其比传统的全注意力明显不那么复杂。在某些情况下，配备了这种情况，STNET的表现优于最先进的模型，其资源的$ 1/10^{th} $。
Channel State Information (CSI) Feedback plays a crucial role in achieving higher gains through beamforming. However, for a massive MIMO system, this feedback overhead is huge and grows linearly with the number of antennas. To reduce the feedback overhead several compressive sensing (CS) techniques were implemented in recent years but these techniques are often iterative and are computationally complex to realize in power-constrained user equipment (UE). Hence, a data-based deep learning approach took over in these recent years introducing a variety of neural networks for CSI compression. Specifically, transformer-based networks have been shown to achieve state-of-the-art performance. However, the multi-head attention operation, which is at the core of transformers, is computationally complex making transformers difficult to implement on a UE. In this work, we present a lightweight transformer named STNet which uses a spatially separable attention mechanism that is significantly less complex than the traditional full-attention. Equipped with this, STNet outperformed state-of-the-art models in some scenarios with approximately $1/10^{th}$ of the resources.
