Enhancing VoLTE Quality using Unsupervised Learning

Abstract

The integration of machine learning (ML) algorithms with data analytics has become essential for optimizing telecommunication services due to the growing complexity of networks. Voice over LTE (VoLTE) is among other technologies that increased this complexity. Hence, optimizing VoLTE to ensure the highest quality-of-service (QoS) attained by end users presents a significant challenge. In this paper, we propose a framework that links key performance indicators (KPIs) with drive test data to gain more insights about the network utility and better manage its resources. The numerical evaluation for our proposed framework demonstrates 25% improvement in the VoLTE QoS compared to other existing approaches.