Machine Learning Based Mobile Network Throughput Classification
This work addresses mobile network management for service providers by enabling cell-level throughput classification using only provider-side data, though it is incremental in method.
The paper tackles the problem of identifying 4G cells with network throughput issues by proposing a model that combines clustering and deep neural networks, trained on limited expert-labeled data, and it outperforms a simple classifier in classification accuracy.
Identifying mobile network problems in 4G cells is more 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. The proposed model takes advantage of clustering and Deep Neural Networks (DNNs). Model parameters are learnt using a small number of expert-labeled data. To achieve case specific classification, we propose a model that contains a multiple clustering models block, for capturing features common for problematic cells. The captured features of this block are then used as an input to a DNN. Experiments show that the proposed model outperforms a simple 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.