Condition monitoring of 3G cellular networks through competitive neural models

We develop an unsupervised approach to condition monitoring of cellular networks using competitive neural algorithms. Training is carried out with state vectors representing the normal functioning of a simulated CDMA2000 network. Once training is completed, global and local normality profiles (NPs)...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2005-09, Vol.16 (5), p.1064-1075
Main Authors: Barreto, G.A., Mota, J.C.M., Souza, L.G.M., Frota, R.A., Aguayo, L.
Format: Article
Language:English
Subjects:
Algorithms
Anomaly detection
Artificial Intelligence
Cell Phone
cellular networks
Competition
competitive learning
Computer Simulation
Condition monitoring
Confidence intervals
Data analysis
Data mining
Information Storage and Retrieval - methods
Internet
Land mobile radio cellular systems
Models, Statistical
Multiaccess communication
Neural Networks (Computer)
normality profiles (NPs)
Pattern Recognition, Automated - methods
Performance evaluation
Quality of service
Quantization
Radio access networks
Signal Processing, Computer-Assisted
Studies
Telecommunications
Testing
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Summary:We develop an unsupervised approach to condition monitoring of cellular networks using competitive neural algorithms. Training is carried out with state vectors representing the normal functioning of a simulated CDMA2000 network. Once training is completed, global and local normality profiles (NPs) are built from the distribution of quantization errors of the training state vectors and their components, respectively. The global NP is used to evaluate the overall condition of the cellular system. If abnormal behavior is detected, local NPs are used in a component-wise fashion to find abnormal state variables. Anomaly detection tests are performed via percentile-based confidence intervals computed over the global and local NPs. We compared the performance of four competitive algorithms [winner-take-all (WTA), frequency-sensitive competitive learning (FSCL), self-organizing map (SOM), and neural-gas algorithm (NGA)] and the results suggest that the joint use of global and local NPs is more efficient and more robust than current single-threshold methods.
ISSN:1045-9227
2162-237X
1941-0093
2162-2388
DOI:10.1109/TNN.2005.853416