In English

Machine Learning for Classifying Cellular Traffic

Isabelle Frölich
Göteborg : Chalmers tekniska högskola, 2017. 62 s.
[Examensarbete på avancerad nivå]

Today’s cellular network is ever growing, making the need for a mechanism that can identify overloads greater each day. In this report a design science research is conducted showcasing the possibilities to use the classification machine learning algorithm naive Bayes to identify signaling overloads in a cellular network node. The research shows that naive Bayes can be used to successfully identify the greater majority of the possible overloads that could occur in a cellular node.

Nyckelord: Machine learning, naive Bayes, cellular network

Publikationen registrerades 2017-06-29. Den ändrades senast 2017-06-29

CPL ID: 250233

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