Title :
Neural analysis of mobile radio access network
Author :
Raivio, Kimmo ; Simula, Olli ; Laiho, Jaana
Author_Institution :
Lab. of Comput. & Inf. Sci., Helsinki Univ. of Technol., Finland
Abstract :
The self-organizing map (SOM) is an efficient tool for visualization and clustering of multidimensional data. It transforms the input vectors on two-dimensional grid of prototype vectors and orders them. The ordered prototype vectors are easier to visualize and explore than the original data. Mobile networks produce a huge amount of spatiotemporal data. The data consists of parameters of base stations (BS) and quality information of calls. There are two alternatives in starting the data analysis. We can build either a general one-cell-model trained using state vectors from all cells, or a model of the network using state vectors with parameters from all mobile cells. In both methods, further analysis is needed to understand the reasons for various operational states of the entire network
Keywords :
data mining; mobile radio; pattern clustering; radio access networks; self-organising feature maps; telecommunication computing; 2D prototype vector grid; base stations; call quality information; data analysis; input vector transformation; mobile radio access network; multidimensional data clustering; multidimensional data visualization; neural analysis; one cell model; operational states; self-organizing map; spatiotemporal data; state vectors; Clustering algorithms; Data analysis; Data engineering; Data mining; Data visualization; Information science; Laboratories; Land mobile radio; Multiaccess communication; Prototypes;
Conference_Titel :
Data Mining, 2001. ICDM 2001, Proceedings IEEE International Conference on
Conference_Location :
San Jose, CA
Print_ISBN :
0-7695-1119-8
DOI :
10.1109/ICDM.2001.989552