Cell Outage Detection in 5G Self-organizing Networks Based on FDA-HMM

The 5G network is anticipated to be more densified in the future, containing numerous heterogeneous cells. Managing the heterogeneous networks (HetNets) becomes challenging and almost unattainable. Self-organizing networks (SONs) are needed to ensure flexiblity and automatic deployment and maintenance of the 5G networks. Automated cell outage detection is a prominent research focus since self-healing SON solutions execute compensation processes to mitigate network disruption. This study presents a Fisher’s discriminant analysis (FDA) to obtain feature vectors with lower dimensionality, which are suitable for hidden Markov model (HMM). The proposed FDA-HMM automatically predicts the present status of 5G base stations (BSs) and determines a cell outage. The proposed FDA-HMM outage detection scheme’s performance is compared with existing algorithms such as the conventional HMM, support vector machine (SVM), and random forest. The results of simulation indicate that the proposed FDA-HMM algorithm effectively detects cell outage with 97.02% accuracy as compared to the exisiting supervised learning methods.