Cellular Network Performance using Machine Learning based Quantitative Association Rule Mining Method

Cellular network performance is often evaluated by key performance indicator (KPI) and key quality indicator (KQI). The association between KQI and KPI is the most critical step to optimize the performance of cellular networks. Traditional association methods between KPI and KQI are based on the end-to-end evaluation. However, these methods require professional engineers to evaluate cellular network performance, and the man-manned evaluation is often inaccurate and labor-consuming, which cannot find out the main caused factor of deterioration networks. In order to solve the problem, we propose a machine learning-based quantitative association rule mining (QARM) method called SWP to associate KPI with KQI. Specifically, we mainly discretize continuous attributes into boolean values and then the association rules of these boolean values are mined by QARM algorithms, such as the Apriori algorithm. Finally, we select the warning intervals and the warning points obtained by SWP method as an optimal output solution. Experiments are conducted over the actual data from telecom operators and the results confirm the feasibility and accuracy of the proposed method