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[2602.13231] An Explainable Failure Prediction Framework for Neural Networks in Radio Access Networks
Summary
This paper presents a framework for explainable failure prediction in neural networks used in radio access networks, enhancing model transparency and performance.
Why It Matters
As 5G networks evolve, ensuring reliable communication is crucial. This framework addresses the black-box nature of predictive models, improving interpretability and scalability, which is vital for network providers aiming to enhance service reliability and efficiency.
Key Takeaways
- Introduces a framework combining explainability with model refinement for RLF prediction.
- Demonstrates that weather data has minimal impact on RLF forecasting, allowing for leaner models.
- Improves model performance with 50% fewer parameters and better F1 scores compared to existing solutions.
- Enhances the interpretability of neural networks, aiding network providers in decision-making.
- Supports integration with advanced architectures like GNN Transformer and LSTM.
Computer Science > Networking and Internet Architecture arXiv:2602.13231 (cs) [Submitted on 28 Jan 2026] Title:An Explainable Failure Prediction Framework for Neural Networks in Radio Access Networks Authors:Khaleda Papry, Francesco Spinnato, Marco Fiore, Mirco Nanni, Israat Haque View a PDF of the paper titled An Explainable Failure Prediction Framework for Neural Networks in Radio Access Networks, by Khaleda Papry and 4 other authors View PDF HTML (experimental) Abstract:As 5G networks continue to evolve to deliver high speed, low latency, and reliable communications, ensuring uninterrupted service has become increasingly critical. While millimeter wave (mmWave) frequencies enable gigabit data rates, they are highly susceptible to environmental factors, often leading to radio link failures (RLF). Predictive models leveraging radio and weather data have been proposed to address this issue; however, many operate as black boxes, offering limited transparency for operational deployment. This work bridges that gap by introducing a framework that combines explainability based feature pruning with model refinement. Our framework can be integrated into state of the art predictors such as GNN Transformer and LSTM based architectures for RLF prediction, enabling the development of accurate and explainability guided models in 5G networks. It provides insights into the contribution of input features and the decision making logic of neural networks, leading to lighter and more scalab...
