基于深度学习的大规模MIMO信道状态信息反馈

An overview of the CSI feedback based on deep learning for massive MIMO systems

大规模多输入多输出(MIMO,multiple-inputmultiple output)技术被认为是下一代移动通信的核心技术之一,其系统增益建立在基站能够精确获知信道状态信息(CSI,channel state information)的基础上。由于天线数量显著增长,传统基于码本或矢量量化的反馈方案面临较大的技术挑战,而深度学习(DL,deep learning)为解决大规模MIMO系统的CSI反馈问题提供了新思路。围绕大规模MIMO系统CSI反馈关键技术展开调研,首先阐述了CSI反馈的研究背景和意义,接着构建大规模MIMO系统模型并分析CSI的稀疏特性,然后详细介绍和比较了国内外将DL技术引入CSI反馈机制中的方案,最后对基于DL的CSI反馈的未来发展趋势做了进一步展望。

The massive multiple-input multiple-output(MIMO)technology is considered to be one of the core technologies of the next generation communication system.To fully utilize the potential gains of MIMO systems,the base station should accurately acquire the channel state information(CSI).Due to the significant increase in the number of antennas,the traditional feedback schemes based on the codebook or vector quantization are faced with great technical challenges.Recently,deep learning(DL)has provided a new idea for solving CSI feedback problems in massive MIMO systems.It was focused on the key technologies of the CSI feedback for massive MIMO systems.Firstly,the background and significance of the CSI feedback were expounded.Then,a model for the massive MIMO system was established and the sparse nature of CSI was analyzed.Several schemes of introducing DL into the CSI feedback mechanism were introduced and compared in detail.Finally,a further prospect on the development trend of the CSI feedback based on DL was made.