Two-Stage Traffic Load Prediction-Based Resource Reservation for Sliced HSR Wireless Networks

被引：1

作者：

Yan, Li ^{[1
]}

Fang, Xuming ^{[1
]}

Fang, Yuguang ^{[2
]}

Li, Yi ^{[3
]}

Xue, Qing ^{[4
]}

机构：

[1] Southwest Jiaotong Univ, Key Lab Informat Coding & Transmiss, Chengdu 610031, Peoples R China

[2] Univ Florida, Dept Elect & Comp Engn, Gainesville, FL 32611 USA

[3] China Acad Railway Sci, Commun Signal Res Inst, Beijing 100082, Peoples R China

[4] Chongqing Univ Posts & Telecommun, Sch Commun & Informat Engn, Chongqing 400065, Peoples R China

来源：

IEEE WIRELESS COMMUNICATIONS LETTERS | 2022年 / 11卷 / 10期

关键词：

Prediction algorithms; Clustering algorithms; Wireless networks; Sensors; Resource management; Rail transportation; Network slicing; HSR wireless networks; network slicing; traffic load prediction; resource reservation; machine learning; SCHEME;

D O I：

10.1109/LWC.2022.3195517

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this letter, we propose a two-stage traffic load prediction scheme for network slices (NSs) in high-speed railway (HSR) wireless networks, where in the first stage, the K-means algorithm is leveraged to cluster traffic flows, and in the second stage, the long-short term memory (LSTM) algorithm is applied to predict the traffic load. Based on the obtained traffic features (including traffic volume and user velocity) and the network radio resource characteristics (including coverage performance and capacity), we design a service-tailored resource reservation mechanism. Simulation results show that our proposed scheme can significantly improve the traffic load prediction accuracy to ensure the NS resource reservation performance.

引用

页码：2145 / 2149

页数：5