Efficient model-and-data based channel estimation algorithm

Kai MEI; Haitao ZHAO; Xiaoran LIU; Jun LIU; Jun XIONG; Baoquan REN; Jibo WEI

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

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Efficient model-and-data based channel estimation algorithm

Papers | 更新时间：2024-06-05

- Efficient model-and-data based channel estimation algorithm
- Journal on Communications Vol. 43, Issue 1, Pages: 59-70(2022)
- 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：
- 基金信息：
  
  The National Natural Science Foundation of China(61931020);The National Natural Science Foundation of China(62101569);The National Natural Science Foundation of China(U19B2024);The National Natural Science Foundation of China(62171449);The National Natural Science Foundation of China(62001483);The Science and Technology Innovation Program of Hunan Province(2020RC2045)
- DOI：10.11959/j.issn.1000-436x.2022019
  CLC： TN929.5
- Online First：2022-01，
  
  Published：25 January 2022
- 稿件说明：
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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：

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.

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references

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