A Novel Framework Leveraging Machine Learning (ML) Techniques, Coupled with Lightweight Deep Learning Mechanisms for Real-Time Call Drop Prediction in Mobile Networks - Ashok, G.V.; Kumari, P. Vasanthi (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Call drops continue to plague mobile networks, negatively impacting user experience and network efficiency. Predicting call drops proactively can enable targeted interventions, improving network performance and customer satisfaction. In the realm of mobile communication networks, ensuring seamless and reliable connectivity is paramount. The escalating demands for data services and the proliferation of mobile devices have placed unprecedented pressure on network operators to maintain optimal performance. This research paper introduces a novel approach to address the persistent challenge of call drops in mobile networks. Leveraging machine learning (ML) techniques with Lightweight Deep Learning mechanisms, coupled with the efficiency of the Lightweight Architecture MobileNetV3, our research aims to pioneer a robust and lightweight solution for call drop prediction, significantly enhancing network reliability and user satisfaction. And leverages readily available network-level features to develop a compact and efficient model capable of real-time call drop prediction with high accuracy. We evaluate the proposed framework on a large-scale dataset from a real-world mobile network, demonstrating significant improvements in prediction accuracy compared to baseline approaches. Furthermore, the lightweight nature of the model enables efficient deployment on edge devices, making it suitable for practical implementation within the network infrastructure. This work paves the way for advanced call drop prediction systems, contributing to enhanced mobile network reliability and user experience.