高效的基于数据与模型的信道估计算法

梅锴; 赵海涛; 刘潇然; 刘军; 熊俊; 任保全; 魏急波

doi:10.11959/j.issn.1000-436x.2022019

您当前的位置：

首页 >

文章列表页 >

高效的基于数据与模型的信道估计算法

学术论文 | 更新时间：2024-06-05

- 高效的基于数据与模型的信道估计算法
- Efficient model-and-data based channel estimation algorithm
- 通信学报 2022年43卷第1期页码：59-70
- 作者机构：
  
  1. 国防科技大学电子科学学院，湖南长沙 410073
  2. 军事科学院系统工程研究院，北京 100076
- 作者简介：
  
  [ "梅锴（1993- ），男，四川仁寿人，国防科技大学博士生，主要研究方向为机器学习、物理层传输技术等" ]
  [ "赵海涛（1981- ），男，山东昌乐人，博士，国防科技大学教授、博士生导师，主要研究方向为认知无线电网络、自组织网络、协同通信等" ]
  [ "刘潇然（1992- ），男，河南洛阳人，博士，国防科技大学讲师，主要研究方向为无线通信信号处理技术、多载波波形设计和智能通信技术" ]
  [ "刘军（1993- ），男，广东广州人，国防科技大学博士生，主要研究方向为机器学习、信息理论和通信信号处理" ]
  [ "熊俊（1987- ），男，江西丰城人，博士，国防科技大学副研究员，主要研究方向为协同通信、物理层安全和网络资源分配等" ]
  [ "任保全（1974- ），男，陕西周至人，博士，军事科学院高级工程师，主要研究方向为物联网、无线通信和移动通信网络技术等" ]
  [ "魏急波（1967- ），男，湖北汉川人，博士，国防科技大学教授、博士生导师，主要研究方向为无线通信网络协议、通信信号处理、协同通信、认知无线电网络等" ]
- 基金信息：
  
  国家自然科学基金资助项目(61931020);国家自然科学基金资助项目(62101569);国家自然科学基金资助项目(U19B2024);国家自然科学基金资助项目(62171449);国家自然科学基金资助项目(62001483);湖南省科技创新计划基金资助项目(2020RC2045)
- DOI：10.11959/j.issn.1000-436x.2022019
  中图分类号： TN929.5
- 网络首发：2022-01，
  
  纸质出版：2022-01-25
- 稿件说明：
移动端阅览
梅锴, 赵海涛, 刘潇然, 等. 高效的基于数据与模型的信道估计算法[J]. 通信学报, 2022,43(1):59-70.

Kai MEI, Haitao ZHAO, Xiaoran LIU, et al. Efficient model-and-data based channel estimation algorithm[J]. Journal on Communications, 2022, 43(1): 59-70.
梅锴, 赵海涛, 刘潇然, 等. 高效的基于数据与模型的信道估计算法[J]. 通信学报, 2022,43(1):59-70. DOI： 10.11959/j.issn.1000-436x.2022019.

Kai MEI, Haitao ZHAO, Xiaoran LIU, et al. Efficient model-and-data based channel estimation algorithm[J]. Journal on Communications, 2022, 43(1): 59-70. DOI： 10.11959/j.issn.1000-436x.2022019.

摘要

针对正交频分复用（OFDM）系统，提出一种新型的数据与模型联合驱动下的信道估计算法。该算法结合一种可在线训练的低复杂度学习型估计方法与线性最小均方误差（LMMSE）估计，既赋予信道估计器通过在线训练提升了估计性能的能力，又借助模型解决了在线生成训练数据会造成额外导频开销的问题，提升了系统效率。仿真结果表明，所提算法在低信噪比下的性能和对实际非理想因素的适应性等方面优于传统信道估计算法。

Abstract

For orthogonal frequency division multiplexing (OFDM) systems

a hybrid model and data driven channel estimation algorithm was proposed.Combined with two existing channel estimation methods

including a low complex learning-based channel estimation method and the linear minimum mean square error (LMMSE) channel estimation

the estimator with the ability was facilitated to employ online training to improve estimation performance.Meanwhile

the pilot overhead consumed by generating online training data was saved due to the use of the model-based method in the proposed algorithm

which improved the spectrum efficiency.The simulation results demonstrate that the proposed algorithm has better performance under low signal-to-noise ratio (SNR) and better adaptation to practical imperfections compared with conventional channel estimation methods.

关键词

Keywords

references

LIU W B , WANG Z D , LIU X H , et al . A survey of deep neural network architectures and their applications [J ] . Neurocomputing , 2017 , 234 : 11 - 26 .

FARSAD N , GOLDSMITH A . Neural network detection of data sequences in communication systems [J ] . IEEE Transactions on Signal Processing , 2018 , 66 ( 21 ): 5663 - 5678 .

SOLTANI M , POURAHMADI V , MIRZAEI A , et al . Deep learning-based channel estimation [J ] . IEEE Communications Letters , 2019 , 23 ( 4 ): 652 - 655 .

NEUMANN D , WIESE T , UTSCHICK W . Learning the MMSE channel estimator [J ] . IEEE Transactions on Signal Processing , 2018 , 66 ( 11 ): 2905 - 2917 .

黄源 , 何怡刚 , 吴裕庭 , 等 . 基于深度学习的压缩感知FDD大规模MIMO系统稀疏信道估计算法 [J ] . 通信学报 , 2021 , 42 ( 8 ): 61 - 69 .

HUANG Y , HE Y G , WU Y T , et al . Deep learning for compressed sensing based sparse channel estimation in FDD massive MIMO systems [J ] . Journal on Communications , 2021 , 42 ( 8 ): 61 - 69 .

CAMMERER S , GRUBER T , HOYDIS J , et al . Scaling deep learning-based decoding of polar codes via partitioning [C ] // Proceedings of GLOBECOM 2017 - 2017 IEEE Global Communications Conference . Piscataway:IEEE Press , 2017 : 1 - 6 .

O’SHEA T , HOYDIS J . An introduction to deep learning for the physical layer [J ] . IEEE Transactions on Cognitive Communications and Networking , 2017 , 3 ( 4 ): 563 - 575 .

RAJ V , KALYANI S . Backpropagating through the air:deep learning at physical layer without channel models [J ] . IEEE Communications Letters , 2018 , 22 ( 11 ): 2278 - 2281 .

WANG H M , JIANG J C , WANG Y N . Model refinement learning and an example on channel estimation with universal noise model [J ] . IEEE Journal on Selected Areas in Communications , 2021 , 39 ( 1 ): 31 - 46 .

HE H T , WEN C K , JIN S , et al . A model-driven deep learning network for MIMO detection [C ] // Proceedings of 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) . Piscataway:IEEE Press , 2018 : 584 - 588 .

GAO X X , JIN S , WEN C K , et al . ComNet:combination of deep learning and expert knowledge in OFDM receivers [J ] . IEEE Communications Letters , 2018 , 22 ( 12 ): 2627 - 2630 .

MEI K , LIU J , ZHANG X Y , et al . A low complexity learning-based channel estimation for OFDM systems with online training [J ] . IEEE Transactions on Communications , 2021 , 69 ( 10 ): 6722 - 6733 .

MEI K , LIU J , ZHANG X C , et al . Performance analysis on machine learning-based channel estimation [J ] . IEEE Transactions on Communications , 2021 , 69 ( 8 ): 5183 - 5193 .

尹浩 , 魏急波 , 赵海涛 , 等 . 一种面向复杂场景的无线通信节点智能适变架构 [J ] . 中国科学:信息科学 , 2021 , 51 ( 2 ): 294 - 304 .

YIN H , WEI J B , ZHAO H T , et al . An intelligent adaptative architecture for wireless communication in complex scenarios [J ] . Scientia Sinica (Informationis) , 2021 , 51 ( 2 ): 294 - 304 .

EDFORS O , SANDELL M , VAN DE BEEK J J , et al . OFDM channel estimation by singular value decomposition [J ] . IEEE Transactions on Communications , 1998 , 46 ( 7 ): 931 - 939 .

DONG X D , LU W S , SOONG A C K . Linear interpolation in pilot symbol assisted channel estimation for OFDM [J ] . IEEE Transactions on Wireless Communications , 2007 , 6 ( 5 ): 1910 - 1920 .

OZDEMIR M K , ARSLAN H . Channel estimation for wireless ofdm systems [J ] . IEEE Communications Surveys ＆ Tutorials , 2007 , 9 ( 2 ): 18 - 48 .

KAY S M . Fundamentals of statistical signal processing [M ] . New Jersey : Prentice Hall International , 1993 .

LIANG N Y , HUANG G B , SARATCHANDRAN P , et al . A fast and accurate online sequential learning algorithm for feedforward networks [J ] . IEEE Transactions on Neural Networks , 2006 , 17 ( 6 ): 1411 - 1423 .

SAVAUX V , LOUËT Y , DJOKO-KOUAM M , et al . Application of a joint and iterative MMSE-based estimation of SNR and frequency-selective channel for OFDM systems [J ] . EURASIP Journal on Advances in Signal Processing,2013 , 2013 :128.

ATHAUDAGE C R N , JAYALATH A D S . Enhanced MMSE channel estimation using timing error statistics for wireless OFDM systems [J ] . IEEE Transactions on Broadcasting , 2004 , 50 ( 4 ): 369 - 376 .

ZHANG Y , DOSHI A , LISTON R , et al . DeepWiPHY:deep learning-based receiver design and dataset for IEEE 802.11ax systems [J ] . IEEE Transactions on Wireless Communications , 2021 , 20 ( 3 ): 1596 - 1611 .

浏览量

1295

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于叠加导频方案的AFDM稀疏信道估计与数据检测

RIS辅助多用户毫米波系统的联合信道估计和阻塞诊断

基于数据特征的成员推理增强攻击方法

基于深度学习的通感一体化系统综述

SnO₂ ∶ Eu³⁺ 纳米晶的水热法制备及发光性能