摘要
为解决正交频分复用(OFDM)系统中由噪声干扰引发的导频污染问题,设计一个基于深度学习的信道估计模型CE-SERNet。将导频位置处最小二乘信道估计值当作低分辨率带噪声图像,作为网络模型输入,利用注意力机制和残差网络进行去噪和恢复高分辨率图像,实现OFDM系统的信道估计。仿真结果表明,所提网络在低导频和高导频条件下都优于现有基于深度学习的方法,相比传统的LS算法和MMSE算法,在估计精度上有较大提升,在不同的信道场景下,拥有较强的鲁棒性能。
To solve the pilot pollution problem caused by noise interference in orthogonal frequency division multiplexing(OFDM)systems,a deep learning based channel estimation model called CE-SERNet was designed.The least square channel estimate at the pilot position was regarded as a low resolution image with noise,which was taken as the network input,and the attention mechanism and residual network were used to de-noise and restore the high resolution image,the channel estimation of OFDM system was realized.Simulation results show that the proposed network is superior to the existing deep learning-based methods at both low and high pilot conditions.Compared with traditional LS and MMSE algorithms,it has significant improvements in estimation accuracy and strong robustness in different channel scenarios.
作者
申滔
朱正发
刘受清
SHEN Tao;ZHU Zheng-fa;LIU Shou-qing(School of Electrical and Information Engineering,Changsha University of Science and Technology,Changsha 410004,China)
出处
《计算机工程与设计》
北大核心
2024年第12期3600-3606,共7页
Computer Engineering and Design
基金
湖南省教育厅一般基金项目(19C0037)
长沙理工大学科研创新基金项目(CLSJCX23067)。
