基于深度学习的正交频分复用系统信道估计  

Channel estimation for OFDM system based on deep learning

作　　者：张昀 周婧 黄经纬 于舒娟[1] 黄丽亚[1] ZHANG Yun;ZHOU Jing;HUANG Jingwei;YU Shujuan;HUANG Liya(College of Electronic and Optical Engineering&College of Flexible Electronics(Future Technology),Nanjing University of Posts and Telecommunications,Nanjing 210023,China)

机构地区：[1]南京邮电大学电子与光学工程学院、柔性电子(未来技术)学院,江苏南京210023

出　　处：《通信学报》2023年第12期124-133,共10页Journal on Communications

基　　金：国家自然科学基金资助项目(No.61977039)。

摘　　要：针对5G系统信号接收子载波间串扰和子符号间干扰问题,提出了一种高效的基于深度学习的信道估计模型。在导频处进行初步估计获得估计信道,并将其视为含噪声的低分辨率图像样本输入信道估计模型,通过学习低分辨率图像与高分辨率图像之间的映射关系,最终去除输入信道的噪声,还原高分辨率信道图像,获得整个信道状态信息。仿真结果表明,该模型不仅延续了传统注意力机制抑制冗余信息的优势,降低了计算开销,还能获得良好的精度和鲁棒性,对各种信道都有较好的估计效果。An efficient channel estimation model based on deep learning was proposed for the problems of inter-carrier interference and inter-symbol interference in 5G system signal reception.The estimated channels were obtained through a preliminary estimation at the pilots.And they were treated as low resolution images containing noise,which were input into the channel estimation model.By learning the mapping relationship between the low resolution images and the high resolution images,the noise in input channels was removed,and the high-resolution channel images were restored to obtain the entire channel state information eventually.The simulation results show that the model not only continues the advantages of traditional attention mechanisms in suppressing redundant information,reduces computa-tional overhead,but also achieves good accuracy and robustness,and has good estimation performance for various channels.

关 键 词：深度学习 信道估计 图像恢复 注意力机制 

分 类 号：TN92[电子电信—通信与信息系统]