An overview of the CSI feedback based on deep learning for massive MIMO systems - Muhan CHEN; Jiajia GUO; Xiao LI; Shi JIN (物联网学报)
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.