基于人工智能的大规模天线信道状态信息反馈研究

黄秋萍; 刘晓峰; 高秋彬; 刘正宣; 金立强; 孙韶辉

doi:10.11959/j.issn.1000-0801.2022051

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基于人工智能的大规模天线信道状态信息反馈研究

专题：5G演进技术 | 更新时间：2024-06-05

- 基于人工智能的大规模天线信道状态信息反馈研究
- Study of channel state information feedback based on artificial intelligence
- 电信科学 2022年38卷第3期页码：74-83
- 作者机构：
  
  1. 中信科移动通信技术股份有限公司，北京 100083
  2. 无线移动通信国家重点实验室（电信科学技术研究院有限公司），北京 100191
  3. 中国信息通信研究院，北京 100191
- 作者简介：
  
  [ "黄秋萍（1987- ），女，博士，中信科移动通信技术股份有限公司高级工程师，主要研究方向为大规模天线技术、移动通信新技术研究与标准制定等" ]
  [ "刘晓峰（1981- ），男，博士，中国信息通信研究院无线通信创新中心副总工程师、正高级工程师，主要研究方向为移动通信系统设计等" ]
  [ "高秋彬（1980- ），男，博士，中信科移动通信技术股份有限公司正高级工程师，主要研究方向为通信系统协议设计、多天线系统、协作传输、信号处理算法以及系统建模与评估等" ]
  [ "刘正宣（1982- ），男，博士，中信科移动通信技术股份有限公司工程师，主要研究方向为大规模多输入多输出和基于人工智能的信道状态信息反馈等" ]
  [ "金立强（1991- ），男，博士，无线移动通信国家重点实验室（电信科学技术研究院有限公司）博士后研究员，主要研究方向为信道编码、AI在通信物理层应用等" ]
  [ "孙韶辉（1972- ），男，博士，中信科移动通信技术股份有限公司副总经理、教授级高级工程师，主要研究方向为移动通信系统设计及多天线技术、卫星通信和定位等关键技术" ]
- 基金信息：
  
  国家重点研发计划项目;The National Key Research and Development Project of China(2020YFB1807100)
- DOI：10.11959/j.issn.1000-0801.2022051
  中图分类号： TP393
- 网络出版日期：2022-03，
  
  纸质出版日期：2022-03-20
- 稿件说明：
移动端阅览
黄秋萍, 刘晓峰, 高秋彬, 等. 基于人工智能的大规模天线信道状态信息反馈研究[J]. 电信科学, 2022,38(3):74-83.

Qiuping HUANG, Xiaofeng LIU, Qiubin GAO, et al. Study of channel state information feedback based on artificial intelligence[J]. Telecommunications science, 2022, 38(3): 74-83.
黄秋萍, 刘晓峰, 高秋彬, 等. 基于人工智能的大规模天线信道状态信息反馈研究[J]. 电信科学, 2022,38(3):74-83. DOI： 10.11959/j.issn.1000-0801.2022051.

Qiuping HUANG, Xiaofeng LIU, Qiubin GAO, et al. Study of channel state information feedback based on artificial intelligence[J]. Telecommunications science, 2022, 38(3): 74-83. DOI： 10.11959/j.issn.1000-0801.2022051.

摘要

信道状态信息（channel state information，CSI）的精确获取是大规模天线发挥效能的关键。在现有的通信系统中，上下行链路互易性不理想时，基于码本进行下行链路的 CSI 反馈。随着天线规模的增大，码本CSI反馈所需要的开销也越来越大。给出了基于人工智能（artificial intelligence，AI）的CSI反馈压缩方法，分析了基于AI的CSI反馈的标准化影响、通信流程与面临的挑战，提供了评估结果。评估结果表明，相对于基于频域基向量压缩的码本CSI反馈，基于AI的CSI反馈在相同的反馈精度下可以显著地降低反馈开销。

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.

关键词

Keywords

references

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