基于元学习的时变信道估计方法  

Meta-learning based time-varying channel estimation method

作　　者：杨丽花 任露露 呼博 邵永琪 聂倩 YANG Lihua;REN Lulu;HU Bo;SHAO Yongqi;NIE Qian(Jiangsu Key Laboratory of Wireless Communication,College of Communication and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,China)

机构地区：[1]南京邮电大学通信与信息工程学院江苏省无线通信重点实验室,南京江苏210003

出　　处：《系统工程与电子技术》2023年第6期1872-1879,共8页Systems Engineering and Electronics

基　　金：江苏省科技厅自然科学基金(BK20191378);江苏省高等学校自然科学研究面上项目(18KJB510034);第11批中国博士后科学基金特别资助项目(2018T110530);国家重大研究计划重点项目(92067201);江苏省重点研发计划(产业前瞻与关键核心技术)(BE2022067,BE2022067-2)资助课题。

摘　　要：为了克服现有的基于深度学习的信道估计方法存在的训练样本与时间开销过大、且离线训练的网络无法适应在实际情况下实时变化的信道环境的问题,提出了一种高速移动正交频分复用(orthogonal frequency division multiplexing, OFDM)系统中的基于元学习的时变信道估计方法。该方法利用模型无关元学习(model-agnostic meta-learning, MAML)方法对不同的信道任务进行线下训练,仅利用少量的训练样本即可使网络充分学习到信道的传输特性,以及具备快速适应新任务的能力,且具有较低的计算复杂度。在线下训练中,该方法将网络的训练目标设置为具有较高精度的信道估计,而非理想的信道信息,增强了估计模型的实用性。另外,该方法仅采用接收的导频信号进行线下训练与线上估计,减少了网络输入样本的数目,进一步降低了计算复杂度。仿真结果表明,所提方法具有较高的估计精度与较低的计算复杂度,且可以快速地适应新的信道环境,适用于在高速移动通信系统中获取时变信道。In the existing deep-learning-based channel estimation methods,there are problems such as large training data and huge time overhead,and the offline training network cannot adapt to the actual real-time changing channel environment.To overcome the problems above,a meta-learning-based time-varying channel estimation method in high-speed mobile orthogonal frequency division multiplexing(OFDM)system is proposed in this paper.The model-agnostic meta-learning(MAML)method is used for offline training for different sub-tasks,so that the network can adequately learn the characteristics of channel transmission and have the ability to quickly adapt to new tasks only by few training samples,which makes it have low computational complexity.In offline training,the proposed method sets the training target of the network as channel estimation with high accuracy rather than ideal channel information,which enhances the practicability of the estimation model.In addition,only the received pilot signal is used for offline training and online estimation,which reduces the number of network input samples and further reduces the computational complexity.Simulation results show that the proposed method has high estimation accuracy and low computational complexity,and it can quickly adapt to the new channel environment,which is suitable for time-varying channels acquisition in high-speed mobile communication systems.

关 键 词：高速移动 正交频分复用 时变信道估计 元学习 

分 类 号：TN929.5[电子电信—通信与信息系统]