高效的基于数据与模型的信道估计算法

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

通信学报 ›› 2022, Vol. 43 ›› Issue (1): 59-70.doi: 10.11959/j.issn.1000-436x.2022019

高效的基于数据与模型的信道估计算法

梅锴¹, 赵海涛¹, 刘潇然¹, 刘军¹, 熊俊¹, 任保全², 魏急波¹

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

修回日期:2022-01-05 出版日期:2022-01-25 发布日期:2022-01-01
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
基金资助:
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)

Efficient model-and-data based channel estimation algorithm

Kai MEI¹, Haitao ZHAO¹, Xiaoran LIU¹, Jun LIU¹, Jun XIONG¹, Baoquan REN², Jibo WEI¹

¹ College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
² Institute of Systems Engineering, Military Academy of Sciences, Beijing 100076, China

Revised:2022-01-05 Online:2022-01-25 Published:2022-01-01
Supported by:
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)

摘要/Abstract

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。

关键词: 机器学习, 数据与模型联合驱动, 正交频分复用系统, 信道估计

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.

Key words: machine learning, hybrid model and data driven, OFDM, channel estimation

中图分类号:

TN929.5

梅锴, 赵海涛, 刘潇然, 刘军, 熊俊, 任保全, 魏急波. 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, Jun LIU, Jun XIONG, Baoquan REN, Jibo WEI. Efficient model-and-data based channel estimation algorithm[J]. Journal on Communications, 2022, 43(1): 59-70.

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参数	取值
传输带宽/MHz	8
OFDM符号周期/μs	125
最大多径时延/μs	8
虚拟子载波数	102
导频符号数	1
子载波间隔/kHz	15.6
CP时间/μs	15.625
FFT点数	512
帧长度	5
数据符号数	4

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

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