基于生成对抗网络的MIMO信道估计方法

华郁秀; 李荣鹏; 赵志峰; 吴建军; 张宏纲

doi:10.11959/j.issn.1000-0801.2021135

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基于生成对抗网络的MIMO信道估计方法

专题：5G+6G | 更新时间：2024-06-05

- 基于生成对抗网络的MIMO信道估计方法
- GAN-based channel estimation for massive MIMO system
- 电信科学 2021年37卷第6期页码：14-22
- 作者机构：
  
  1. 浙江大学信息与电子工程学院，浙江杭州 310027
  2. 之江实验室，浙江杭州 311121
  3. 华为技术有限公司，上海 200120
- 作者简介：
  
  [ "华郁秀（1993- ），男，浙江大学信息与电子工程学院博士生，主要研究方向为无线资源管理、网络切片、深度学习、强化学习" ]
  [ "李荣鹏（1989- ），男，浙江大学信息与电子工程学院副教授，主要研究方向为智能通信网络、网络智能、网络切片" ]
  [ "赵志峰（1975- ），男，浙江大学信息与电子工程学院教授，之江实验室科研发展部主任，主要研究方向为认知无线电、无线mesh 网络和SDN 在无线通信中的应用" ]
  [ "吴建军（1977- ），男，华为技术有限公司未来网络架构实验室主任，主要研究方向为6G网络架构、5G端到端切片、下一代移动通信标准及产业发展" ]
  [ "张宏纲（1967- ），男，浙江大学电子与信息工程学院教授，主要研究方向为认知无线电、绿色通信和下一代异构蜂窝网络架构" ]
- 基金信息：
  
  国家重点研发计划项目;The National Key Research and Development Program of China(2020YFB1804804SCI);国家自然科学基金资助项目;The National Natural Science Foundation of China(61731002);国家自然科学基金资助项目;The National Natural Science Foundation of China(62071425);浙江省重点研发计划项目;Zhejiang Key Research and Development Plan(2019C01002);浙江省重点研发计划项目;Zhejiang Key Research and Development Plan(2019C03131);华为合作项目：之江实验室资助项目;Huawei Cooperation Project, The Project sponsored by Zhejiang Lab(2019LC0AB01);浙江省自然科学基金资助项目;The Natural Science Foundation of Zhejiang Provincial of China(LY20F010016)
- DOI：10.11959/j.issn.1000-0801.2021135
  中图分类号： TP393
- 网络出版日期：2021-06，
  
  纸质出版日期：2021-06-20
- 稿件说明：
移动端阅览
华郁秀, 李荣鹏, 赵志峰, 等. 基于生成对抗网络的MIMO信道估计方法[J]. 电信科学, 2021,37(6):14-22.

Yuxiu HUA, Rongpeng LI, Zhifeng ZHAO, et al. GAN-based channel estimation for massive MIMO system[J]. Telecommunications science, 2021, 37(6): 14-22.
华郁秀, 李荣鹏, 赵志峰, 等. 基于生成对抗网络的MIMO信道估计方法[J]. 电信科学, 2021,37(6):14-22. DOI： 10.11959/j.issn.1000-0801.2021135.

Yuxiu HUA, Rongpeng LI, Zhifeng ZHAO, et al. GAN-based channel estimation for massive MIMO system[J]. Telecommunications science, 2021, 37(6): 14-22. DOI： 10.11959/j.issn.1000-0801.2021135.

摘要

作为 5G 的一项关键技术，大规模多输入多输出（MIMO）系统通过在基站配备大量天线可以显著提高频谱效率和能效。然而，在大规模MIMO 系统中，精确的信道估计面临严峻挑战。为了在导频序列长度小于发射天线数量以及信道噪声强烈的情况下进行精确的估计信道，提出了一种基于生成对抗网络的大规模MIMO 信道估计方法N2N-GAN。N2N-GAN首先对接收端的导频信号进行去噪，然后使用条件生成对抗网络根据去噪后的导频信号估计信道矩阵。仿真实验证明，与传统的信道估计算法和基于深度学习的算法相比， N2N-GAN 对环境噪声具有更高的鲁棒性，而且可以适应更少导频符号和更多天线数量的场景。

Abstract

As a key technology of 5G

massive MIMO system can significantly improve spectrum efficiency and energy efficiency by equipping a large number of antennas in base stations.However

in massive MIMO system

accurate channel estimation faces severe challenges.In order to estimate the channel accurately when the pilot sequence length is smaller than the number of transmitting antennas and the channel noise is strong

the estimation method N2N-GAN was proposed.N2N-GAN firstly denoised the pilot channel at the receiving end

and then used the conditional generative adversarial network to estimate the channel matrix according to the denoised pilot signal.Simulation experiments show that N2N-GAN achieves better robustness against noise compared with traditional channel estimation algorithms and deep learning-based methods.Meanwhile

it can adapt to scenarios with fewer pilot symbols and more antennas.

关键词

Keywords

references

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