Machine learning based session drop prediction in LTE networks and its SON aspects

Daróczy, Bálint Zoltán and Vaderna, P and Benczúr, András (2015) Machine learning based session drop prediction in LTE networks and its SON aspects. In: 81st IEEE Vehicular Technology Conference, VTC Spring 2015. IEEE Vehicular Technology Conference (2015). IEEE, Glasgow, pp. 1-5. ISBN 9781479980888 10.1109/VTCSpring.2015.7145925


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Abnormal bearer session release (i.e. bearer session drop) in cellular telecommunication networks may seriously impact the quality of experience of mobile users. The latest mobile technologies enable high granularity real-time reporting of all conditions of individual sessions, which gives rise to use data analytics methods to process and monetize this data for network optimization. One such example for analytics is Machine Learning (ML) to predict session drops well before the end of session. In this paper a novel ML method is presented that is able to predict session drops with higher accuracy than using traditional models. The method is applied and tested on live LTE data offline. The high accuracy predictor can be part of a SON function in order to eliminate the session drops or mitigate their effects. © 2015 IEEE.

Item Type: Book Section
Uncontrolled Keywords: Wireless telecommunication systems; Traditional models; Self Organizing Network (SON); Quality of experience (QoE); Network optimization; mobile technology; High granularity; Data analytics; Cellular telecommunication; Telecommunication networks; Mobile telecommunication systems; Learning systems; Forecasting; Drops; Artificial intelligence; Session drop; Self-organizing network (SON); machine learning
Subjects: Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions: Informatics Laboratory
SWORD Depositor: MTMT Injector
Depositing User: MTMT Injector
Date Deposited: 31 Jan 2016 08:33
Last Modified: 31 Jan 2016 08:33

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