Root Cause Analysis of Low Throughput Situations Using Boosting Algorithms and the TreeShap Analysis

Detecting and diagnosing the root cause of failures in mobile networks is an increasingly demanding and time-consuming task, given its technological growing complexity. This paper focuses on predicting and diagnosing low User Downlink (DL) Average Throughput situations, using supervised learning and the Tree Shapley Additive Explanations (SHAP) method. To fulfill this objective, Boosting classification models are used to predict a failure/non-failure binary label. The influence of each counter on the overall model’s predictive performance is performed based on the TreeSHAP method. From the implementation of this technique, it is possible to identify the main causes of low throughput, based on the analysis of the most critical counters in fault detection. Furthermore, from the identification of these counters, it is possible to define a system for diagnosing the most probable throughput degradation cause. The described methodology allowed not only to identify and quantify low throughput situations in a live network due to the occurrence of misadjusted configuration parameters, radio problems and network capacity problems, but also to outline a process for solving them.