5G Network Slicing Prediction Using Explainable Artificial Intelligence(X-AI)

The emergence of 5G technology has transformed network architecture, with network slicing technology standing out as a crucial feature for managing diverse and demanding applications. This innovative technology allows the creation of multiple virtual networks within a single physical infrastructure, each dedicated to specific needs such as latency, bandwidth, and reliability. These capabilities are vital for supporting services like enhanced mobile broadband, ultra-reliable low-latency communications, and massive machine-type communications. Predicting the optimal configuration of network slices is challenging due to the complex and dynamic nature of 5G environments. While traditional methods often lack the necessary accuracy, machine learning models have shown promise in addressing this challenge. However, the adoption of these models introduces the challenge of interpretability, as understanding how they reach their predictions is often difficult. In this work, we have proposed that Explainable Artificial Intelligence techniques such as Permutation Feature Importance and Partial Dependency Plots could be employed to enhance the transparency of prediction of ML models. These methods provide insights into the decision-making processes of ML models, making their predictions more interpretable and trustworthy for critical 5G network management tasks.