Long Short-Term Memory Network for Wireless Channel Prediction

被引：7
|
作者：
Tong, Xiaoyun [1 ,2 ,3 ]
Sun, Songlin [1 ,2 ,3 ]
机构：
[1] Beijing Univ Posts & Telecommun, Sch Informat & Commun Engn, Beijing, Peoples R China
[2] Beijing Univ Posts & Telecommun, Key Lab Trustworthy Distributed Comp & Serv BUPT, Minist Educ, Beijing, Peoples R China
[3] Beijing Univ Posts & Telecommun, Natl Engn Lab Mobile Network Secur, Beijing, Peoples R China
来源：
SIGNAL AND INFORMATION PROCESSING, NETWORKING AND COMPUTERS | 2018年 / 473卷
关键词：
Wireless channel prediction; Channel state information; Long short-term memory network;
D O I：
10.1007/978-981-10-7521-6_3
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In modern wireless systems, channel prediction is an effective way to overcome the feedback delay of channel state information (CSI). When the receiver performs adaptive transmission based on the feedback CSI, the channel prediction algorithm can reduce the system overhead by predicting the future CSI. In this paper, we provide a long short-term memory (LSTM) network for wireless channel prediction. This method can get a smaller prediction error than other intelligence methods. Experiments show that the LSTM model has a lower normalized mean square error (NMSE) and less running time than support vector machine, artificial neural network, and recurrent neural network prediction approaches.
引用
收藏
页码：19 / 26
页数：8
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