Understanding the challenges of the call drop prediction problem in IP Multimedia Subsystem Networks

• Pedro Victor Dos Santos Matias UFAM
• Ricardo Miranda Filho UFAM
• Rosiane de Freitas UFAM

DOI: https://doi.org/10.5753/eniac.2024.245284

Resumo

Call drops in mobile networks using IMS (IP Multimedia Subsystem) technologies like Voice over LTE (VoLTE), Voice over New Radio (VoNR), and Voice over Wi-Fi (VoWiFi) present significant challenges to maintaining Quality of Service (QoS) and Quality of Experience (QoE). These failures often occur due to network congestion, weak signals, or issues related to software problems and complex situations. This study evaluates the effectiveness of machine learning models—Logistic Regression, Decision Tree, and XGBoost—in predicting call drops using a large dataset from Android devices, which had an imbalanced distribution of data. XGBoost achieved the highest overall accuracy but struggled with detecting rare call drops due to data imbalance. Although resampling techniques improved the detection of these call drops, they decreased overall accuracy, which remains a challenge. The proprietary nature of the dataset, which only provides information at the moment of disconnection, limits understanding of the entire call performance and the changes that occur during the call. Future work should focus on improving the data collection process and exploring deep learning techniques to capture complex patterns and improve prediction accuracy.

Palavras-chave: Call Drop Classification, Supervised Learning, Mobile Network Failures

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