Abstract: Low-performing cells, serving a telecom deployment in a given geographical area, are commonly identified by the telecom operators based on one or more Network KPIs computed for each of the cells being part of the deployment. However, most of these KPIs describe the overall state of affairs of the cell without any insight into the user usage profile of the cell. The present disclosure proposes a novel method to adjust the network KPIs based on scores of the macro cell or the small cell (101) with the user usage profile of the cell to obtain the user correlated network scores of the cells and prioritize the low-performing cells in the network in order of degree of impact on users and their usage. Another aspect of this disclosure is that it shall predict and compute a realistic user score to get predictive insights into user profiles for solutions and preventive measures.

Abstract: Low-performing cells, serving a telecom deployment in a given geographical area, are commonly identified by the telecom operators based on one or more Network KPIs computed for each of the cells being part of the deployment. However, most of these KPIs describe the overall state of affairs of the cell without any insight into the user usage profile of the cell. The present disclosure proposes a novel method to adjust the network KPIs based on scores of the macro cell or the small cell (101) with the user usage profile of the cell to obtain the user correlated network scores of the cells and prioritize the low-performing cells in the network in order of degree of impact on users and their usage. Another aspect of this disclosure is that it shall predict and compute a realistic user score to get predictive insights into user profiles for solutions and preventive measures.