On Service Traffic Prediction of Base Stations along High-speed Railway

被引：3

作者：

Hu, Hengrui ^{[1
,2
]}

Xiong, Jian ^{[1
,2
]}

Cheng, Peng ^{[3
]}

Shi, Zhiping ^{[4
]}

Gu, Chaoyu ^{[2
]}

机构：

[1] Shanghai Jiao Tong Univ, Dept Elect Engn, Shanghai, Peoples R China

[2] Univ Sydney, Natl Key Lab Sci & Technol Commun, Sydney, NSW, Australia

[3] Univ Sydney, Sch Elect & Informat Engn, Sydney, NSW, Australia

[4] Sch Elect & Informat Engn Elect Sci & Technol Chi, Chengdu, Peoples R China

来源：

2020 IEEE INTERNATIONAL SYMPOSIUM ON BROADBAND MULTIMEDIA SYSTEMS AND BROADCASTING (BMSB) | 2020年

基金：

中国国家自然科学基金;

关键词：

feature extraction; high-speed railway; LSTM neural network; traffic prediction; FORECASTING-MODEL;

D O I：

10.1109/BMSB49480.2020.9379598

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper provides a novel feature extraction algorithm for the service traffic prediction of base stations along high-speed railways. Considering that the traffic of base stations has both time and space characteristics, a feature extraction algorithm is proposed to extract the spatial characteristics of neighboring base stations. Long Short-Term Memory neural network (LSTM) with the feature extraction algorithm are compared with the traditional Recursive Neural Network (RNN) algorithm and Autoregressive Integrated Moving Average algorithm (ARIMA), the results prove that the LSTM algorithm is better than the RNN and ARIMA algorithms, and the prediction performance of the LSTM is better after adding the feature extraction algorithm.

引用

页数：5

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