Utilization of advanced analysis methods in UMTS networks

The scope of this paper is to introduce new analysis and visualization methods for WCDMA cellular networks. The proposed examples are mainly based on the self-organizing map (SOM) method, but also other neural and statistical methods are equally applicable. The main motivation for advanced methods is to increase the abstraction level from the raw network measurements, i.e. radio access network language, to network functional areas or a language closer to the business of network operator. Furthermore the vast amount of quality of service (QoS) and service combinations, that 3G will enable, require effective data handling procedures.

[1]  J. Nazuno.  Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999 , 2000 .

[2]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[3]  J. A. Flanagan.  Chapter 16 Topologically ordered neural networks , 2001 .

[4]  Teuvo Kohonen,et al.  Self-Organizing Maps, Second Edition , 1997, Springer Series in Information Sciences.

[5]  H. W. Veen,et al.  Handbook of Biological Physics , 1996 .

[6]  Olli Simula,et al.  Neural analysis of mobile radio access network , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[7]  D. Signorini,et al.  Neural networks , 1995, The Lancet.

[8]  Kimmo Hätönen,et al.  A computer host-based user anomaly detection system using the self-organizing map , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[9]  T Kohonen.  The Self-Organising Map, a Possible Model of Brain Maps , 1997 .