基于人工智能的大规模天线信道状态信息反馈研究

doi:10.11959/j.issn.1000-0801.2022051

Abstract

Abstract:

Accurate acquisition of CSI (channel state information) is the key to the performance of massive MIMO.In current communication systems, when the reciprocity of uplink and downlink is not ideal, codebook-based CSI feedback is used for downlink CSI acquisition.With the increase of antenna scale, codebook-based CSI feedback needs more and more overhead.The CSI feedback compression method based on AI (artificial intelligence) was presented, and the standardization impact, communication process and challenges of CSI feedback based on AI were analyzed.Besides, evaluation results were provided.The evaluation results show that compared with codebook-based CSI feedback based on frequency domain basis vector compression, CSI feedback based on AI can significantly reduce the feedback cost at the same feedback accuracy.

Key words: massive MIMO, AI, CSI feedback

CLC Number:

TP393

Qiuping HUANG, Xiaofeng LIU, Qiubin GAO, Zhengxuan LIU, Liqiang JIN, Shaohui SUN. Study of channel state information feedback based on artificial intelligence[J]. Telecommunications Science, 2022, 38(3): 74-83.

Figures/Tables 8

References 25

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参数名称	值
双工方式/波形	FDD/OFDM
场景	密集城区
频点范围	2 GHz，上下行载频间隔为200 MHz
站间距	200 m
gNB的天线配置	32端口:(M, N, P, M_g, N_g,M_p, N_p) = (8, 8, 2, 1, 1, 2, 8), (dH,dV) = (0.5, 0.8)λ
UE的天线配置	4 端口: (M, N, P, M_g, N_g,M_p, N_p)=(1,2,2,1,1,1,2), (dH,dV) = (0.5, 0.5)λ
基站发送功率	49 dBm
基站天线高度	29 m
系统带宽	10 MHz
子载波间隔	15 kHz
CQI子带	4 PRB
传输层数	最大12层
CSI 反馈	CSI 反馈周期：5 ms；调度时延：4 ms
业务类型	FTP 模式1, 包大小为 0.5 MB
资源利用率	70%
UE 分布	室内80%（3 km/h）, 室外20%（30 km/h）
UE 接收算法	MMSE-IRC

Study of channel state information feedback based on artificial intelligence

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