%0 Conference Proceedings
%T Root Cause Analysis of Reduced Accessibility in 4G Networks
%+ Universidade de Aveiro
%+ Department of Electronics, Telecommunications and Informatics [Aveiro] (DETI)
%+ Altice Labs [Aveiro]
%+ Instituto de Telecomunicações [Lisboa, Portugal]
%A Ferreira, Diogo
%A Senna, Carlos
%A Salvador, Paulo
%A Cortesão, Luís
%A Pires, Cristina
%A Pedro, Rui
%A Sargento, Susana
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 2nd International Conference on Machine Learning for Networking (MLN)
%C Paris, France
%Y Selma Boumerdassi
%Y Éric Renault
%Y Paul Mühlethaler
%I Springer International Publishing
%3 Machine Learning for Networking
%V LNCS-12081
%P 117-133
%8 2019-12-03
%D 2019
%R 10.1007/978-3-030-45778-5_9
%K Cellular networks
%K Root cause analysis
%K Machine learning
%Z Computer Science [cs]
%Z Computer Science [cs]/Networking and Internet Architecture [cs.NI]Conference papers
%X The increased programmability of communication networks makes them more autonomous, and with the ability to actuate fast in response to users and networks’ events. However, it is usually a difficult task to understand the root cause of the network problems, so that autonomous actuation can be provided in advance.This paper analyzes the probable root causes of reduced accessibility in 4G networks, taking into account the information of important Key Performance Indicators (KPIs), and considering their evolution in previous time-frames. This approach resorts to interpretable machine learning models to measure the importance of each KPI in the decrease of the network accessibility in a posterior time-frame.The results show that the main root causes of reduced accessibility in the network are related with the number of failure handovers, the number of phone calls and text messages in the network, the overall download volume and the availability of the cells. However, the main causes of reduced accessibility in each cell are more related to the number of users in each cell and its download volume produced. The results also show the number of PCA components required for a good prediction, as well as the best machine learning approach for this specific use case.
%G English
%Z TC 6
%2 https://inria.hal.science/hal-03266468/document
%2 https://inria.hal.science/hal-03266468/file/487577_1_En_9_Chapter.pdf
%L hal-03266468
%U https://inria.hal.science/hal-03266468
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC6
%~ IFIP-LNCS-12081
%~ IFIP-MLN