Machine Learning Based Mobile Network Throughput Classification

Lauri Alho, Adrian Burian, Janne Helenius, Joni Pajarinen

Abstract: Identifying mobile network problems in 4G cells becomes challenging when the complexity of the network increases and privacy concerns limit the information content of the data. This paper proposes a data-driven model for identifying 4G cells that have fundamental network throughput problems. If problematic cells could be fixed the total throughput of the network would increase by an expert identified 8% in our data set gathered from real 4G cells. The proposed model takes advantage of clustering and deep neural networks and requires only a small amount of expert-labeled data. To achieve case-specific classification, we use a model that contains a block that has multiple clustering models for capturing features common for problematic cells. A deep neural network then uses as an input the captured features of the clustering block. Experiments show that the proposed model outperforms a simple baseline classifier in identifying cells with network throughput problems. To the best of the authors’ knowledge, there is no related research where network throughput classification is performed on the cell level with information gathered only from the service provider’s side.