  This paper addresses the challenges of detecting anomalies in cellular
networks in an interpretable way and proposes a new approach using variational
autoencoders (VAEs) that learn interpretable representations of the latent
space for each Key Performance Indicator (KPI) in the dataset. This enables the
detection of anomalies based on reconstruction loss and Z-scores. We ensure the
interpretability of the anomalies via additional information centroids (c)
using the K-means algorithm to enhance representation learning. We evaluate the
performance of the model by analyzing patterns in the latent dimension for
specific KPIs and thereby demonstrate the interpretability and anomalies. The
proposed framework offers a faster and autonomous solution for detecting
anomalies in cellular networks and showcases the potential of deep
learning-based algorithms in handling big data.
