联合BP神经网络与基扩展模型的信道预测算法  

Channel Prediction Method Joint BP Neural Network with Basis Expansion Model

作　　者：杨丽花[1] 聂倩 呼博 江婷 YANG Lihua;NIE Qian;HU Bo;JIANG Ting(Jiangsu Key Laboratory of Wireless Communication,Nanjing University of Posts and Telecommunications,Nanjing 210003,China;School of Electronic Information Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)

机构地区：[1]南京邮电大学江苏省无线通信重点实验室,南京210003 [2]南京航空航天大学电子信息工程学院,南京210016

出　　处：《北京邮电大学学报》2023年第3期13-18,共6页Journal of Beijing University of Posts and Telecommunications

基　　金：江苏省科技项目(BK20191378)。

摘　　要：针对高速移动的多输入多输出正交频分复用系统,提出了一种低复杂度的联合反向传播(BP)神经网络与基扩展模型的时变信道预测算法。为了降低计算复杂度,采用基扩展模型对信道进行建模,并通过对信道基系数进行线下训练与线上预测以获取未来时刻的信道信息。在线下训练中,首先基于历史接收的导频信号获取信道的基系数估计;然后构造训练样本,并将其送入BP神经网络训练中,以获取信道预测网络模型。在线上预测时,基于训练得到网络模型与历史基系数估计,从而获取未来时刻的时域信道。仿真实验结果表明,所提算法的计算复杂度较低,且预测精度较高,适用于未来高速移动环境下时变信道信息的高效获取。For high⁃speed mobile multiple⁃input multiple⁃output orthogonal frequency division multiplexing system,a low⁃complexity time⁃varying channel prediction method joint the back propagation(BP)neural network with basis expansion model is proposed.To reduce the computational complexity,the basis expansion model is employed to model the time varying channel,and the channel information at a future time is obtained by the offline training and online prediction of the channel base coefficient.During offline training,the proposed method first acquires the channel base coefficient by the received pilots.Then to obtain the channel prediction network model,the training sample is constructed and sent into the BP neural network for training.During the online prediction,based on the network model and historical base coefficient estimation obtained by the training,the proposed method can obtain the time domain channel at the future time.The simulation results show that the proposed method has lower computational complexity and higher prediction accuracy than the existing methods,which is suitable for the efficient acquisition of time⁃varying channel information in the future high⁃speed mobile environment.

关 键 词：高速移动 基扩展模型 反向传播神经网络 时变信道预测 

分 类 号：TN929.5[电子电信—通信与信息系统]