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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

View graph of relations

Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)2145-2149
Journal / PublicationIEEE Wireless Communications Letters
Volume11
Issue number10
Online published2 Aug 2022
Publication statusPublished - Oct 2022
Externally publishedYes

Abstract

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.

Research Area(s)

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

Citation Format(s)

Two-stage Traffic Load Prediction-Based Resource Reservation for Sliced HSR Wireless Networks. / Yan, Li; Fang, Xuming; Fang, Yuguang et al.
In: IEEE Wireless Communications Letters, Vol. 11, No. 10, 10.2022, p. 2145-2149.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review