Q-Learning Based Intelligent Traffic Steering in Heterogeneous Network

被引：3

作者：

Adachi, Koichi ^{[1
]}

Li, Maodong ^{[1
]}

Tan, Peng Hui ^{[1
]}

Zhou, Yuan ^{[1
]}

Sun, Sumei ^{[1
]}

机构：

[1] ASTAR, Inst Infocomm Res, 1 Fusionopolis Way,21-01 Connexis South Tower, Singapore 138632, Singapore

来源：

2016 IEEE 83RD VEHICULAR TECHNOLOGY CONFERENCE (VTC SPRING) | 2016年

关键词：

Traffic steering; Heterogeneous network; Machine learning;

D O I：

10.1109/VTCSpring.2016.7504436

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：

In this paper, we present a user equipment (UE) based distributed traffic steering mechanism between long-term evolution (LTE) and Wi-Fi networks. An agent residing in each UE evaluates the traffic condition of the network it is currently connecting to and makes the traffic steering decision. The evaluation is either periodic or event-driven such as access denial in the admission control due to network congestion. The learning mechanism enables each UE to use the locally available information at the UE and select the proper network under dynamic network conditions. The computer simulation results show that the proposed mechanism achieves low outage probability and small number of network switching with even less information than or almost the same as the existing method. We have also implemented the proposed traffic steering mechanism as an APP on android platform and verified that the proposed mechanism works effectively in real-time testing.

引用

页数：5