Proposal for bandwidth prediction using Hybrid Division Model

被引：1

作者：

Cadovski, Ognjen ^{[1
]}

Ilic, Velibor ^{[1
]}

Rikic, Bojan ^{[1
]}

Kovacevic, Branimir ^{[1
]}

机构：

[1] RT RK Inst Comp Based Syst, Novi Sad, Serbia

来源：

2021 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE) | 2021年

关键词：

automotive; machine learning; prediction; model; bandwidth; random forest; TIME;

D O I：

10.1109/ICCE50685.2021.9427751

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The Bandwidth value is one of the most significant parameters on internet signal quality. Finding the parameters with the most significant influence on the bandwidth value allows us to predict it using Random Forest Machine Learning algorithm, which proves to be the algorithm most adequate for bandwidth prediction. The problem with using the Random Forest model for bandwidth prediction is that different parameters figure in various types of networks. In order to overcome the mentioned shortcomings in performances, we introduce Hybrid Division Model, which is based on Random Forest Machine Learning algorithm. The role of the Hybrid Division model is to create a separate model for each type of network and operator, which will be analyzed in this paper.

引用

页数：2

共 8 条

[1] Real-time Bandwidth Prediction and Rate Adaptation for Video Calls over Cellular Networks [J].