Alarm Prediction in LTE Networks

Antenna line alarms are a significant indicator for failing components in LTE base stations. By predicting such alarms it is possible to prepare for system repairs or for system resets in advance, which reduces the maintenance downtime for LTE operators when a hardware failure actually occurs. In this paper we present results from an antenna line alarm prediction method in a real LTE network. The prediction model is based on the behavior of a large set of performance counters (PMs) collected in a timeseries in the LTE base stations. We create the alarm prediction model by using the random forest search method and we reach and accuracy of 67-72% From this, we pinpoint the most influential PMs for predicting different types of alarms and we discuss how to use these PMs in future research.

[1]  Tin Kam Ho,et al.  A Data Complexity Analysis of Comparative Advantages of Decision Forest Constructors , 2002, Pattern Analysis & Applications.

[2]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[3]  Lorenza Giupponi,et al.  A Reinforcement Learning Based Solution for Self-Healing in LTE Networks , 2014, 2014 IEEE 80th Vehicular Technology Conference (VTC2014-Fall).

[4]  Tapani Ristaniemi,et al.  Detection of Sleeping Cells in LTE Networks Using Diffusion Maps , 2011, 2011 IEEE 73rd Vehicular Technology Conference (VTC Spring).

[5]  Andreas Mitschele-Thiel,et al.  Minimizing Handover Performance Degradation Due to LTE Self Organized Mobility Load Balancing , 2013, 2013 IEEE 77th Vehicular Technology Conference (VTC Spring).

[6]  Raquel Barco,et al.  On the Potential of Handover Parameter Optimization for Self-Organizing Networks , 2013, IEEE Transactions on Vehicular Technology.

[7]  András A. Benczúr,et al.  Machine Learning Based Session Drop Prediction in LTE Networks and Its SON Aspects , 2015, 2015 IEEE 81st Vehicular Technology Conference (VTC Spring).

[8]  Adnan Abu-Dayya,et al.  A framework for classification of Self-Organising network conflicts and coordination algorithms , 2013, 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[9]  James Irvine,et al.  An Advanced SOM Algorithm Applied to Handover Management Within LTE , 2013, IEEE Transactions on Vehicular Technology.

[10]  Emil Bergner.  Unsupervised Learning of Traffic Patterns in Self-Optimizing 4th Generation Mobile Networks. , 2012 .

[11]  Ulf Lindqvist,et al.  Detecting anomalies in cellular networks using an ensemble method , 2013, Proceedings of the 9th International Conference on Network and Service Management (CNSM 2013).