DCAD: Dynamic Cell Anomaly Detection for operational cellular networks

The Self-Organizing Networks (SON) concept includes the functional area known as self-healing, which aims to automate the detection and diagnosis of, and recovery from, network degradations and outages. In this paper, we present Dynamic Cell Anomaly Detection (DCAD), a tool that implements an adaptive ensemble method for modeling cell behavior [5], [6]. DCAD uses Key Performance Indicators (KPIs) from real cellular networks to determine cell-performance status; enables KPI data exploration; visualizes anomalies; reduces the time required for successful detection of anomalies; and accepts user input.

[1]  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).

[2]  Bernhard Pfaff,et al.  VAR, SVAR and SVEC Models: Implementation Within R Package vars , 2008 .

[3]  Szabolcs Nováczki.  An improved anomaly detection and diagnosis framework for mobile network operators , 2013, 2013 9th International Conference on the Design of Reliable Communication Networks (DRCN).

[4]  Stefan Rüping,et al.  SVM Kernels for Time Series Analysis , 2001 .

[5]  Henning Sanneck,et al.  LTE Self-Organising Networks (SON): Network Management Automation for Operational Efficiency , 2012 .

[6]  Ulf Lindqvist,et al.  On the feasibility of deploying cell anomaly detection in operational cellular networks , 2014, 2014 IEEE Network Operations and Management Symposium (NOMS).