A Data-Driven Traffic Steering Algorithm for Optimizing User Experience in Multi-Tier LTE Networks

被引：22

作者：

Gijon, Carolina ^{[1
]}

Toril, Matias ^{[1
]}

Luna-Ramirez, Salvador ^{[1
]}

Luisa Mari-Altozano, Maria ^{[1
]}

机构：

[1] Univ Malaga, Commun Engn Dept, E-29071 Malaga, Spain

来源：

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY | 2019年 / 68卷 / 10期

基金：

欧盟地平线“2020”;

关键词：

Handover; Quality of experience; Long Term Evolution; Heuristic algorithms; Throughput; Load management; Mobile network; Quality of Experience; Self-Organizing Network; Reference Signal Received Quality; load balancing; HANDOVER MANAGEMENT; OPTIMIZATION; CHALLENGES; QUALITY; 5G;

D O I：

10.1109/TVT.2019.2933068

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：

Multi-tier cellular networks are a cost-effective solution for capacity enhancement in urban scenarios. In these networks, effective mobility strategies are required to assign users to the most adequate layer. In this paper, a data-driven self-tuning algorithm for traffic steering is proposed to improve the overall Quality of Experience (QoE) in multi-carrier Long Term Evolution (LTE) networks. Traffic steering is achieved by changing Reference Signal Received Quality (RSRQ)-based inter-frequency handover margins. Unlike classical approaches considering cell-aggregated counters to drive the tuning process, the proposed algorithm relies on a novel indicator, derived from connection traces, showing the impact of handovers on user QoE. Method assessment is carried out in a dynamic system-level simulator implementing a real multi-carrier LTE scenario. Results show that the proposed algorithm significantly improves QoE figures obtained with classical load balancing techniques.

引用

页码：9414 / 9424

页数：11

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