Time-varying channel prediction method based on LSTM neural networks under basis expansion model

被引：0
|
作者：
Nie Q. [1 ]
Yang L. [1 ]
Hu B. [1 ]
Ren L. [1 ]
机构：
[1] Jiangsu Key Laboratory of Wireless Communication, Nanjing University of Posts and Telecommunications, Nanjing
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2022年 / 44卷 / 09期
关键词：
basis expansion model (BEM); high-speed mobile; long short-term memory (LSTM) neural network; time-varying channel prediction;
D O I：
10.12305/j.issn.1001-506X.2022.09.33
中图分类号：
学科分类号：
摘要：
For high-speed mobile orthogonal frequency division multiplexing system, a time-varying channel prediction method based on long short-term memory (LSTM) neural network under basis expansion model (BEM) is proposed. To reduce the modeling error of the traditional BEM, according to the strong correlation characteristics of the wireless channel at the same location for the different trains in a high-speed mobile environment, the optimal basis function is obtained by the channel sate information of historical time, and it is used to model the channel. Then, the channel information at the future time is obtained by the offline training and online prediction of the channel base coefficient via LSTM neural network, which greatly reduces the computational complexity. In offline training, to enhance the practicality of the prediction model, the channel estimation, rather than the ideal channel information, is set to the approximation objective of the network. The simulation results show that the proposed method has lower computational complexity and better prediction accuracy comparing with the existing methods. © 2022 Chinese Institute of Electronics. All rights reserved.
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页码：2971 / 2977
页数：6
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