Investigation and practical assessment of alarm correlation methods for the use in GSM access networks

This paper compares and assesses several alarm correlation methods for their suitability and performance in global systems for mobile communications (GSM). The assessment criteria used reflect the special circumstances found in these networks. A high importance is given to the aspects related to a practical and feasible network management. Of the neural networks investigated, the cascade correlation learning algorithm performs best. This approach is compared with correlation techniques proposed in the literature: rule-based diagnosis, model based diagnosis and alarm correlation using codebooks. It is shown that for alarm correlation in a GSM access network the proposed cascade correlation approach is superior to the other correlation techniques.