Quantized Auto Encoder-Based Anomaly Detection for Multivariate Time Series Data in 5G Networks

Abstract

With the arrival of 5G technology, networks face critical challenges in detecting anomalies that can significantly impact performance and reliability. This paper introduces QAED (Quantized Auto Encoder Detector), a novel deep learning approach for anomaly detection in 5G networks with three key innovations: 1) a vector quantization mechanism that effectively captures discrete network states, 2) a kernel density estimation preprocessing step that enables detection of both outliers and distribution shifts, and 3) an integrated architecture that processes multivariate time series data in a unified framework. We provide a detailed evaluation of our model across 5G data scenarios, demonstrating its enhanced accuracy and efficiency in anomaly detection compared to existing state-of-the-art methods, with gains of up to 8%.