By Topic

Performance evaluation of anomaly detection in cellular core networks using self-organizing map

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Sukkhawatchani, P. ; Sch. of Telecommun. Eng., Suranaree Univ. of Technol., Nakhon Ratchasima ; Usaha, W.

One of the preconditions to guarantee the quality of service (QoS) of cellular mobile networks is the rapid and accurate detection of key performance index (KPI) anomalies. This paper applies a neural network algorithm called self-organizing map (SOM) to monitor traffic measurement anomalies collected from an actual cellular network service provider. Results show that the SOM algorithm is able to detect global anomalies as well as identify which KPIs of the core network are abnormal. These results suggest that SOM can indeed help facilitate human operation, making it easier, faster and more efficient for human to troubleshoot, optimize or correct the configuration of the core network.

Published in:

Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2008. ECTI-CON 2008. 5th International Conference on  (Volume:1 )

Date of Conference:

14-17 May 2008