高速移动环境下基于RM-Net的大规模MIMO CSI反馈算法

廖勇; 王世义

doi:10.11959/j.issn.1000-436x.2022097

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高速移动环境下基于RM-Net的大规模MIMO CSI反馈算法

学术论文 | 更新时间：2024-06-05

- 高速移动环境下基于RM-Net的大规模MIMO CSI反馈算法
- CSI feedback algorithm based on RM-Net for massive MIMO systems in high-speed mobile environment
- 通信学报 2022年43卷第5期页码：166-176
- 作者机构：
  
  重庆大学微电子与通信工程学院，重庆 400044
- 作者简介：
  
  [ "廖勇（1982- ），男，四川自贡人，博士，重庆大学副研究员、博士生导师，主要研究方向为下一代无线通信、人工智能、区块链及其在无线通信中的应用等" ]
  [ "王世义（1996- ），男，山东淄博人，重庆大学硕士生，主要研究方向为无线通信CSI反馈" ]
- 基金信息：
  
  国家自然科学基金资助项目(61501066);重庆市自然科学基金资助项目(cstc2019jcyj-msxmX0017)
- DOI：10.11959/j.issn.1000-436x.2022097
  中图分类号： TN929.5
- 网络出版日期：2022-05，
  
  纸质出版日期：2022-05-25
- 稿件说明：
移动端阅览
廖勇, 王世义. 高速移动环境下基于RM-Net的大规模MIMO CSI反馈算法[J]. 通信学报, 2022,43(5):166-176.

Yong LIAO, Shiyi WANG. CSI feedback algorithm based on RM-Net for massive MIMO systems in high-speed mobile environment[J]. Journal on communications, 2022, 43(5): 166-176.
廖勇, 王世义. 高速移动环境下基于RM-Net的大规模MIMO CSI反馈算法[J]. 通信学报, 2022,43(5):166-176. DOI： 10.11959/j.issn.1000-436x.2022097.

Yong LIAO, Shiyi WANG. CSI feedback algorithm based on RM-Net for massive MIMO systems in high-speed mobile environment[J]. Journal on communications, 2022, 43(5): 166-176. DOI： 10.11959/j.issn.1000-436x.2022097.

摘要

针对高速移动环境信道特征复杂多变，同时存在加性噪声和非线性效应的影响，提出一种残差混合网络（RM-Net）的大规模MIMO CSI反馈算法。RM-Net通过学习高速移动信道的空间结构与时间相关性，具备去除大规模MIMO信道噪声的能力，能显著提高CSI压缩率与恢复质量。系统仿真结果表明，RM-Net可消除高速移动场景加性噪声的影响，学习并适应稀疏、双选衰落信道特征，在高压缩率与低信噪比条件下依然具有较好的性能表现，所提算法性能大幅优于其他基于压缩感知（CS）和深度学习（DL）的CSI反馈算法。

Abstract

Aiming at the complex and changeable channel characteristics in high-speed mobile environment

and the influence of additive noise and nonlinear effects

a residual mixing network (RM-Net) for massive MIMO CSI feedback was proposed.By learning the spatial structure and temporal correlation of high-speed mobile channel

the network was able to remove massive MIMO channel noise

and the CSI compression rate and recovery quality could be significantly improved.System simulation results show that RM-Net can eliminate the influence of additive noise in high-speed mobile scenarios

learn and adapt to the channel characteristics of sparse and double-selective fading channels

and still has good performance under the conditions of high compression rate and low signal-to-noise ratio.The proposed algorithm performance is much better than other CS-based and DL-based CSI feedback algorithms.

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references

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