Performance Based Cells Classification in Cellular Network using CDR Data

In the advent of ultra-dense networks with unprecedented complex and heterogeneous infrastructure, the role of automation in network optimization becomes vital for sustaining the target performance. In this work, we address the challenge of identifying and classifying sub-par performing nodes in near-real time through a machine-learning inspection of streaming performance indicators from multiple probe points. We present a novel K-means-based solution for classifying node performance over a sliding time segment and further categorizing the type of failure. The K-means solution first identifies the performance instances of interest. These are then inspected in a second clustering round for automated performance labeling. Next, the labeled data-set is employed to train a Support Vector Machine based classifier that is continuously classifying incoming performance instances from the network. The method is tested using a real network data set comprising call detail records. The results advocate the potential of our method for effectively and accurately identifying and classifying performance degradation in any node in the network.