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5G cellular networks come with many new features compared to the legacy cellular networks, such as network data analytics function (NWDAF), which enables the network operators to either implement their own machine learning (ML) based data analytics methodologies or integrate third-party solutions to their networks. In this paper, the structure and the protocols of NWDAF that are defined in the 3rd Generation Partnership Project (3GPP) standard documents are first described. Then, cell-based synthetic data set for 5G networks based on the fields defined by the 3GPP specifications is generated. Further, some anomalies are added to this data set (e.g., suddenly increasing traffic in a particular cell), and then these anomalies within each cell, subscriber category, and user equipment are classified. Afterward, three ML models, namely, linear regression, long-short term memory, and recursive neural networks are implemented to study behaviour information estimation (e.g., anomalies in the network traffic) and network load prediction capabilities of NWDAF. For the prediction of network load, three different models are used to minimize the mean absolute error, which is calculated by subtracting the actual generated data from the model prediction value. For the classification of anomalies, two ML models are used to increase the area under the receiver operating characteristics curve, namely, logistic regression and extreme gradient boosting. According to the simulation results, neural network algorithms outperform linear regression in network load prediction, whereas the tree-based gradient boosting algorithm outperforms logistic regression in anomaly detection. These estimations are expected to increase the performance of the 5G network through NWDAF.
Sevgican et al. (Mon,) studied this question.