  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.
