A Data-Driven Traffic Steering Algorithm for Optimizing User Experience in Multi-Tier LTE Networks
| Autor: | María Luisa Marí-Altozano, Matías Toril, Carolina Gijón, Salvador Luna-Ramírez |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer Networks and Communications
business.industry Computer science Aerospace Engineering 020302 automobile design & engineering 020206 networking & telecommunications Throughput 02 engineering and technology Load balancing (computing) Data-driven Load management 0203 mechanical engineering User experience design Handover Automotive Engineering 0202 electrical engineering electronic engineering information engineering Cellular network Quality of experience Electrical and Electronic Engineering Multi tier business Algorithm |
| Zdroj: | IEEE Transactions on Vehicular Technology. 68:9414-9424 |
| ISSN: | 1939-9359 0018-9545 |
| DOI: | 10.1109/tvt.2019.2933068 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|