Hybrid K-Mean Clustering and Markov Chain for Mobile Network Accessibility and Retainability Prediction †

Abstract

To provide reliable services, mobile network operators (MNOs) continuously collect vital mobile network performance data to monitor and analyze the functioning of their radio access networks (RANs). RAN is a critical infrastructure for mobile networks and its performance is measured by key performance indicators (KPIs) such as accessibility, retainability, availability, integrity, and mobility. The standard practice is that network managers utilize KPIs to identify failures or unusual events that can significantly degrade the quality of service delivery and the end-users’ experiences. However, taking corrective steps based on monitored performance parameters is a reactive approach that contributes to network and service degradations until corrective actions are taken. With the monitoring and automation of RAN infrastructure performance in mind, this paper presents the Markov chain, a widely used probabilistic modeling approach, as a systematic method for jointly predicting network accessibility and retainability status, two of the crucial RAN performance measures. The novel joint prediction is proposed to have a single operation for both accessibility and retainability. Real-time hourly KPIs data was collected from 1530 cells (base stations) run by an operator’s network in Addis Ababa, Ethiopia, for 4 months, from 1 November 2020 to 28 February 2021. The cells are scattered across the capital city, where factors such as land use, settlement patterns, and customers behaviors differ. To capture the spatial variation of the KPIs without escalating the computational complexity much, the dataset is separated into six clusters using the K-mean clustering approach. The Markov chain KPIs status prediction models are formulated on a cluster level. The results reveal that the proposed models can predict the KPIs status with 94.61 percent accuracy. Because the data is already available and can be collected at any time using the operator’s network management system (NMS), this is a cost-effective technique to proactively improve mobile network performance.