Clutter-Type Classification of the Telco BTS Units with the Use of Limited Input Information

In the paper, the real-world problem of predictive modeling is demonstrated. The objective is to associate automatically a clutter type to a telco network element (NE) using a limited input information on its local configuration and performance. For experimental data recorded in operator networks preprocessing actions have been applied which revealed the feature importance in the context of separation of network state space into several baskets (in reference to the clutter type systematics used in telecommunication). Then, selected machine learning models have been designed and applied to solve the problem of binary and multiclassification. Obtained results for the case of real network data measured in two city areas (North America) exhibited that it is possible to differentiate reliably (accuracy close to 90%) between rural and urban characterization of network elements, and even identify three classes of clutters, i.e. rural, urban (including suburban), and dense urban, with reliability significant for the telecommunication applications. Reported results can be further improved and used then for automation of network analytics, its optimization and management. The main contribution concerns the problem algorithmization and data processing in application to automation in telecommunication.