基于稀疏贝叶斯学习的GFDM系统联合迭代信道估计与符号检测

王莹; 于永海; 郑毅; 林彬

doi:10.12263/DZXB.20220480

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基于稀疏贝叶斯学习的GFDM系统联合迭代信道估计与符号检测

学术论文 | 更新时间：2025-12-11

- 基于稀疏贝叶斯学习的GFDM系统联合迭代信道估计与符号检测
- Iterative Channel Estimation and Symbol Detection for GFDM Systems Based on Sparse Bayesian Learning
- 电子学报 2024年52卷第5期页码：1496-1505
- 作者机构：
  
  大连海事大学信息科学技术学院，辽宁大连 116026
- 作者简介：
  
  [ "王莹男，1968年9月生，河北保定人.现为大连海事大学信息科学技术学院教授、硕士生导师.主要研究方向为移动通信技术、无线自组织网络.E-mail: wangying@dlmu.edu.cn" ]
  [ "于永海男，1997年8月生，内蒙古赤峰人.现为大连海事大学信息科学技术学院硕士研究生.主要研究方向为移动通信技术.E-mail: 1835759984@qq.com" ]
  [ "郑毅男，1996年12月生，山东日照人.现为大连海事大学信息科学技术学院硕士研究生.主要研究方向为移动通信技术、强化学习.E-mail: zhengyi347388256@126.com" ]
  [ "林彬女，1977年6月生，辽宁大连人.现为大连海事大学信息科学技术学院教授、博士生导师.主要研究方向为海上无线通信与网络技术、大规模网络规划与优化.E-mail: Binlin@dlmu.edu.cn" ]
- 基金信息：
  
  国家重点研发计划(2019YFE0111600);国家自然科学基金(61971083;51939001)
- DOI：10.12263/DZXB.20220480
  中图分类号： TN91;
- 收稿：2022-04-29，
  
  修回：2023-07-23，
  
  纸质出版：2024-05-25
- 稿件说明：
移动端阅览
王莹,于永海,郑毅,等. 基于稀疏贝叶斯学习的GFDM系统联合迭代信道估计与符号检测[J]. 电子学报,2024,52(05):1496-1505.

WANG Ying, YU Yong-hai, ZHENG Yi, et al. Iterative Channel Estimation and Symbol Detection for GFDM Systems Based on Sparse Bayesian Learning[J]. Acta Electronica Sinica, 2024, 52(05): 1496-1505.
王莹,于永海,郑毅,等. 基于稀疏贝叶斯学习的GFDM系统联合迭代信道估计与符号检测[J]. 电子学报,2024,52(05):1496-1505. DOI：10.12263/DZXB.20220480

WANG Ying, YU Yong-hai, ZHENG Yi, et al. Iterative Channel Estimation and Symbol Detection for GFDM Systems Based on Sparse Bayesian Learning[J]. Acta Electronica Sinica, 2024, 52(05): 1496-1505. DOI：10.12263/DZXB.20220480

摘要

针对当前广义频分复用（Generalized Frequency Division Multiplexing，GFDM）系统时变信道估计精度低的问题，提出基于稀疏贝叶斯学习的GFDM系统联合信道估计与符号检测算法.具体地，采用无干扰导频插入的GFDM多重响应信号模型，在稀疏贝叶斯学习框架下，结合期望最大化算法（Expectation-Maximization，EM）和卡尔曼滤波与平滑算法实现块时变信道的最大似然估计；基于信道状态信息的估计值进行GFDM符号检测，并通过信道估计与符号检测的迭代处理逐步提高信道估计与符号检测的精度.仿真结果表明，所提算法能够获得接近完美信道状态信息条件下的误码率性能，且具有收敛速度快、对多普勒频移鲁棒性高等优点.

Abstract

In order to improve the accuracy of time-varying channel estimation in generalized frequency division multiplexing (GFDM) systems

a joint iterative channel estimation and symbol detection algorithm for GFDM systems using sparse Bayesian learning is proposed. Specifically

we use a GFDM multi-response signal model with non-interfering pilot insertion. Under the sparse Bayesian learning framework

we combine the expectation-maximization (EM) algorithm and the Kalman filter and smoothing algorithm to realize the maximum likelihood estimation of the block time-varying channel. Consequently

GFDM symbols are detected based on the estimated channel state information (CSI)

and the accuracy of the channel estimation and symbol detection is progressively improved through the iterative processing of the channel estimation and symbol detection. Simulation results demonstrate that the proposed algorithm can achieve better bit error rate (BER) performance close to that under perfect CSI conditions

and it has the advantages of fast convergence speed and high robustness to Doppler frequency shift.

关键词

Keywords

references

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