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.

该研究采用变分自编码器的方法解决了在移动网络中可解释的异常检测问题，该方法可以学习每个关键性能指标的可解释表示，使用重构误差和 Z 分数检测异常，通过附加信息中心增强表征学习，从而实现了异常可解释性和自主检测，同时展示了深度学习算法处理大数据的潜力。

基于变分自编码器学习概念的蜂窝网络可解释异常检测

Interpretable Anomaly Detection in Cellular Networks by Learning  Concepts in Variational Autoencoders

Maximum likelihood training has favorable statistical properties and is
popular for generative modeling, especially with normalizing flows. On the
other hand, generative autoencoders promise to be more efficient than
normalizing flows due to the manifold hypothesis. In this work, we introduce
successful maximum likelihood training of unconstrained autoencoders for the
first time, bringing the two paradigms together. To do so, we identify and
overcome two challenges: Firstly, existing maximum likelihood estimators for
free-form networks are unacceptably slow, relying on iteration schemes whose
cost scales linearly with latent dimension. We introduce an improved estimator
which eliminates iteration, resulting in constant cost (roughly double the
runtime per batch of a vanilla autoencoder). Secondly, we demonstrate that
naively applying maximum likelihood to autoencoders can lead to divergent
solutions and use this insight to motivate a stable maximum likelihood training
objective. We perform extensive experiments on toy, tabular and image data,
demonstrating the competitive performance of the resulting model. We call our
model the maximum likelihood autoencoder (MLAE).

本文首次成功地将自动编码器与极大似然训练法结合，通过提出改进的评估器并克服训练中的两个挑战，获得了具有竞争力性能的最大似然自动编码器（MLAE）模型。