Hybrid Deep Learning Ensemble with Dynamic Fusion for Reliable Anomaly Detection in Operational LTE RANs - Kahoul, Abd El Madjid; Bouraoui, Zakaria; Zarour, Karim; Boumezbeur, Insaf (Journal of Telecommunications and the Digital Economy, 2025)
Monitoring long-term evolution (LTE) network performance is increasingly complex due to rapidly growing data volumes and the diversity of quality-of-service indicators. Traditional monitoring approaches relying on static thresholds and manual key performance indicator (KPI) analysis often fail to detect multidimensional, evolving anomalies. We propose instead a hybrid deep ensemble learning framework for anomaly detection and diagnosis in Radio Access Networks (RANs). This framework integrates four complementary architectures: (i) a convolutional autoencoder (CAE); (ii) a Bidirectional Long Short-Term Memory AutoEncoder (BLSTM-AE); (iii) a transformer autoencoder (transformer AE); and (iv) a bidirectional LSTM forecaster, generating various anomaly scores. These scores are dynamically fused across frequency bands and processed with an Isolation Forest (IF) to produce the final anomaly judgment. An evaluation on real LTE data from Algerian mobile networks (three months, 1650 base stations, hourly KPIs) demonstrates the effectiveness of the proposed approach, achieving a maximum F1 score of 93.89%, an improvement of up to 9.5% over the best individual model. SHapley Additive exPlanations (SHAP)-based explainability analysis reveals that key operational indicators related to mobility and resource use drive the model’s decisions. This work provides a practical, interpretable hybrid framework validated on confidential operational data from a national operator under region-specific conditions.