基于深度学习的广义频分复用系统时频双选择信道估计

王莹; 任军; 史可; 林彬

doi:10.11959/j.issn.1000-436x.2021188

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基于深度学习的广义频分复用系统时频双选择信道估计

学术通信 | 更新时间：2024-06-05

- 基于深度学习的广义频分复用系统时频双选择信道估计
- Doubly-selective channel estimation for generalized frequency division multiplexing systems based on deep learning
- 通信学报 2021年42卷第10期页码：233-242
- 作者机构：
  
  1. 大连海事大学信息科学技术学院，辽宁大连 116026
  2. 鹏程实验室网络通信研究中心，广东深圳 518052
- 作者简介：
  
  [ "王莹（1968− ），男，河北保定人，博士，大连海事大学教授，主要研究方向为移动通信理论、无线自组网等" ]
  [ "任军（1997− ），男，安徽淮北人，大连海事大学硕士生，主要研究方向为多载波通信技术" ]
  [ "史可（1998− ），女，吉林榆树人，大连海事大学硕士生，主要研究方向为多载波通信技术" ]
  [ "林彬（1977− ），女，辽宁大连人，博士，大连海事大学教授，主要研究方向为无线网络优化理论" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2019YFE0111600);国家自然科学基金资助项目(61971083);国家自然科学基金资助项目(51939001);大连市科技创新基金资助项目(2019J11CY015)
- DOI：10.11959/j.issn.1000-436x.2021188
  中图分类号： TN92
- 网络出版日期：2021-10，
  
  纸质出版日期：2021-10-25
- 稿件说明：
移动端阅览
王莹, 任军, 史可, 等. 基于深度学习的广义频分复用系统时频双选择信道估计[J]. 通信学报, 2021,42(10):233-242.

Ying WANG, Jun REN, Ke SHI, et al. Doubly-selective channel estimation for generalized frequency division multiplexing systems based on deep learning[J]. Journal on communications, 2021, 42(10): 233-242.
王莹, 任军, 史可, 等. 基于深度学习的广义频分复用系统时频双选择信道估计[J]. 通信学报, 2021,42(10):233-242. DOI： 10.11959/j.issn.1000-436x.2021188.

Ying WANG, Jun REN, Ke SHI, et al. Doubly-selective channel estimation for generalized frequency division multiplexing systems based on deep learning[J]. Journal on communications, 2021, 42(10): 233-242. DOI： 10.11959/j.issn.1000-436x.2021188.

摘要

广义频分复用（GFDM）系统存在固有的子载波间干扰和子符号间干扰，在时频双选择信道下，会产生严重的导频污染现象，使基于导频的信道估计性能显著下降。为此，提出一种基于深度学习的 GFDM 系统信道估计框架，将离散导频位置处最小二乘信道估计值构成低分辨图像作为网络输入，利用深度残差网络恢复信道时频响应的高分辨图像，实现GFDM系统的信道估计。设计了基于深度残差网络的GFDM时频双选择信道估计算法的仿真系统，通过离线训练获得深度残差网络的最优参数。仿真结果表明，所提算法能够得到接近于最小均方误差信道估计的精度和误码率性能，并具有稳健的多普勒频移泛化能力。

Abstract

There exist intrinsic inter-carrier interference and inter-subsymbol interference in generalized frequency division multiplexing (GFDM) systems.Under condition of time-frequency doubly selective channels

severe effects of pilot contamination would occur and lead to significant performance degradation for the pilot-based channel estimations.To this end

a channel estimation framework for GFDM systems based on deep learning was proposed

which took the low-resolution image constructed with the least squares estimated channel gains of the pilot symbols as input.Consequently

a high-resolution image about the channel time-frequency response was recovered through a deep residual network

and the channel estimation was achieved for GFDM systems.A simulation system for the proposed GFDM time-frequency doubly selective channel estimation algorithm based on deep residual network was developed

and the optimal parameters of the deep residual network were determined through an offline training process.Simulation results show that the proposed algorithm can achieve better performance near to minimum mean square error (MMSE) estimation in terms of estimation error and bit error rate (BER)

and has robust Doppler frequency shift generalization capability.

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references

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