CSI feedback algorithm based on deep unfolding for massive MIMO systems

Yong LIAO; Gang CHENG; Yujie LI

doi:10.11959/j.issn.1000-436x.2022237

您当前的位置：

首页 >

文章列表页 >

CSI feedback algorithm based on deep unfolding for massive MIMO systems

Papers | 更新时间：2024-06-05

- CSI feedback algorithm based on deep unfolding for massive MIMO systems
- Journal on Communications Vol. 43, Issue 12, Pages: 77-88(2022)
- 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：
- 基金信息：
  
  The Natural Science Foundation of Chongq-ing(cstc2019jcyj-msxmX0017)
- DOI：10.11959/j.issn.1000-436x.2022237
  CLC： TN929.5
- Online First：2022-12，
  
  Published：25 December 2022
- 稿件说明：
移动端阅览
Yong LIAO, Gang CHENG, Yujie LI. CSI feedback algorithm based on deep unfolding for massive MIMO systems[J]. Journal on Communications, 2022, 43(12): 77-88.
DOI：

Yong LIAO, Gang CHENG, Yujie LI. CSI feedback algorithm based on deep unfolding for massive MIMO systems[J]. Journal on Communications, 2022, 43(12): 77-88. DOI： 10.11959/j.issn.1000-436x.2022237.

摘要

针对现阶段大规模MIMO系统中基于深度学习的信道状态信息（CSI）反馈算法待训练参数过多、可解释性不强的问题，提出了2种基于深度展开的CSI反馈算法。一种是基于可学习参数的近似消息传递（AMP）算法，该算法利用深度学习中的可学习参数将AMP算法中阈值函数的阈值和Onsager校正项的参数替换，增强了阈值函数在应对非严格稀疏数据时的非线性能力。另一种是基于卷积网络的 AMP 算法，该算法将阈值函数模块替换为卷积残差学习模块，利用该模块去除 AMP 算法中每轮迭代产生的高斯随机噪声。仿真分析表明，所提算法具有比AMP算法更好的CSI反馈表现，其中基于卷积网络的AMP算法具有比基于深度学习的代表性方法更优异的CSI重构性能。

Abstract

In order to solve the problem that the channel state information (CSI) feedback algorithm based on deep learning in massive MIMO systems at present had too many parameters to be trained and could not be explained well

two CSI feedback algorithms based on depth expansion were proposed.The first one was approximate message delivery (AMP) algorithm based on learnable parameters.The learnable parameters in deep learning were used to replace the threshold value of the threshold function in the AMP algorithm and the parameters of the Onsage correction term.The nonlinear ability of threshold function in dealing with non-strict sparse data was enhanced.The other was the AMP algorithm based on convolutional network

which replaced the threshold function module with the convolutional residual learning module

and used the module to remove the Gaussian random noise generated by each iteration of the AMP algorithm.Simulation results show that the proposed two algorithms have better CSI feedback performance than AMP algorithm

and the AMP algorithm based on convolutional network has better CSI reconstruction performance than the representative method based on deep learning.

关键词

Keywords

references

LI C G , LIU P , ZOU C , et al . Spectral-efficient cellular communications with coexistent one- and two-hop transmissions [J ] . IEEE Transactions on Vehicular Technology , 2016 , 65 ( 8 ): 6765 - 6772 .

BARRIAC G , MADHOW U . Space-time communication for OFDM with implicit channel feedback [J ] . IEEE Transactions on Information Theory , 2004 , 50 ( 12 ): 3111 - 3129 .

RAO X B , LAU V K N . Distributed compressive CSIT estimation and feedback for FDD multi-user massive MIMO systems [J ] . IEEE Transactions on Signal Processing , 2014 , 62 ( 12 ): 3261 - 3271 .

LIANG P Z , FAN J C , SHEN W H , et al . Deep learning and compressive sensing-based CSI feedback in FDD massive MIMO systems [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 8 ): 9217 - 9222 .

WEN C K , SHIH W T , JIN S . Deep learning for massive MIMO CSI feedback [J ] . IEEE Wireless Communications Letters , 2018 , 7 ( 5 ): 748 - 751 .

WANG Y , YAO H , ZHAO S . Auto-encoder based dimensionality reduction [J ] . Neurocomputing , 2016 , 184 : 232 - 242 .

GUO J J , WEN C K , JIN S , et al . Convolutional neural network-based multiple-rate compressive sensing for massive MIMO CSI feedback:design,simulation,and analysis [J ] . IEEE Transactions on Wireless Communications , 2020 , 19 ( 4 ): 2827 - 2840 .

廖勇 , 王帅 , 孙宁 . 快时变FDD大规模MIMO系统智能CSI反馈方法 [J ] . 通信学报 , 2021 , 42 ( 7 ): 211 - 219 .

LIAO Y , WANG S , SUN N . Intelligent CSI feedback method for fast time-varying FDD massive MIMO system [J ] . Journal on Communications , 2021 , 42 ( 7 ): 211 - 219 .

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 .

LIAO Y , WANG S Y . 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 , 李玉杰 . 一种轻量化低复杂度的FDD大规模MIMO系统CSI反馈方法 [J ] . 电子学报 , 2022 , 50 ( 5 ): 1211 - 1217 .

LIAO Y , LI Y J . Lightweight and low complexity CSI feedback method for FDD massive MIMO systems [J ] . Acta Electronica Sinica , 2022 , 50 ( 5 ): 1211 - 1217 .

JAGANNATH A , JAGANNATH J , MELODIA T . Redefining wireless communication for 6G:signal processing meets deep learning with deep unfolding [J ] . IEEE Transactions on Artificial Intelligence , 2021 , 2 ( 6 ): 528 - 536 .

QING C J , CAI B , YANG Q Y , et al . Deep learning for CSI feedback based on superimposed coding [J ] . IEEE Access , 2019 , 7 : 93723 - 93733 .

LIU Z Y , ROSARIO M D , DING Z . A Markovian model-driven deep learning framework for massive MIMO CSI feedback [J ] . IEEE Transactions on Wireless Communications , 2021 , 21 ( 2 ): 1214 - 1228 .

GUO J H , WANG L , LI F , et al . CSI feedback with model-driven deep learning of massive MIMO systems [J ] . IEEE Communications Letters , 2021 , 26 ( 3 ): 547 - 551 .

DONOHO D L , MALEKI A , MONTANARI A . Message-passing algorithms for compressed sensing [J ] . Proceedings of the National Academy of Sciences , 2009 , 106 ( 45 ): 18914 - 18919 .

BALATSOUKAS-STIMMING A , STUDER C . Deep unfolding for communications systems:a survey and some new directions [C ] // Proceedings of IEEE International Workshop on Signal Processing Systems (SiPS) . Piscataway:IEEE Press , 2019 : 266 - 271 .

MONTANARI A , ELDAR Y C , KUTYNIOK G . Graphical models concepts in compressed sensing [M ] . London : Cambridge University , 2012 .

LOHIT S , KULKARNI K , KERVICHE R , et al . Convolutional neural networks for noniterative re-construction of compressively sensed images [J ] . IEEE Transactions on Computational Imaging , 2018 , 4 ( 3 ): 326 - 340 .

FRANOIS C , . Xception:deep learning with depthwise separable convolutions [C ] // Proceedings of The IEEE International Conference on Computer Vision and Pattern Recognition:Piscataway:IEEE Press , 2017 : 1251 - 1258 .

ZHANG K , ZUO W M , CHEN Y J , et al . Beyond a gaussian denoiser:residual learning of deep CNN for image denoising [J ] . IEEE Transactions on Image Processing , 2017 , 26 ( 7 ): 3142 - 3155 .

LIU L F , OESTGES C , POUTANEN J , et al . The COST 2100 MIMO channel model [J ] . IEEE Wireless Communications , 2012 , 19 ( 6 ): 92 - 99 .

ZHANG Z J , . Improved Adam optimizer for deep neural networks [C ] // Proceedings of IEEE/ACM 26th International Symposium on Quality of Service . Piscataway:IEEE Press , 2018 : 1 - 2 .

Views

2404

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

CSI feedback algorithm for massive MIMO systems based on SFNet

CSI feedback algorithm based on RM-Net for massive MIMO systems in high-speed mobile environment

Review of unsupervised anomaly detection techniques for medical imaging

Target visibility prediction method based on cost-sensitive CNN-BiLSTM network

Multi-modal semantics aided mmWave beam prediction method in industrial haze environment

Related Author

ZHANG Yun

HUANG Jingwei

XU Sunwu

GAO Gui

YU Shujuan

ZHAO Shengmei

Shiyi WANG

Zhao Yingcheng

Related Institution

College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications

School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications

School of Computer Science and Engineering, Xi’an University of Technology

Shaanxi Key Laboratory for Network Computing and Security Technology

Human Machine Integration Intelligent Robot Shaanxi Provincial University Engineering Research Center

AI问答

⁰