基于自注意力机制的大规模MIMO信道状态信息特征向量反馈方法

Self-attention mechanism-based CSI eigenvector feedback for massive MIMO

大规模多输入多输出(MIMO)系统可以为5G和未来无线通信系统提供令人满意的频谱效率的增益。在频分双工(FDD)模式下,需要将下行信道状态信息(CSI)的特征向量精确地反馈到基站侧以获得这种增益。为了提升下行CSI特征向量的反馈精度,提出了一种基于自注意力机制的CSI反馈方法SA-CsiNet。SA-CsiNet通过分别在编、解码器部署自注意力模块实现CSI的特征提取和重构。仿真实验结果表明,相较于码本和传统的深度学习CSI反馈方案而言,SA-CsiNet能够提供更高的CSI重建精度。

Massive multiple-input multiple-output(MIMO)system can provide satisfying gain of spectrum efficiency for 5G and future wireless communication systems.In frequency-division duplex(FDD)mode,downlink channel state information(CSI)needs to be accurately fed back to the base station side to obtain this gain.To improve the feedback accuracy of downlink CSI eigenvector,a self-attention mechanism-based CSI feedback method named SA-CsiNet was proposed.SA-CsiNet respectively deployed self-attention modules at the encoder and the decoder to achieve feature extraction and reconstruction of CSI.Experimental results show that compared with codebook-based and conventional deep learning-based CSI feedback approaches,SA-CsiNet provides higher reconstruction accuracy of CSI.