Transform-Based Multiresolution Decomposition for Degradation Detection in Cellular Networks.

Sergio Fortes,Inmaculada Serrano,Raquel Barco,Pablo Muñoz

doi:10.3390/s20195645

Sergio Fortes, Inmaculada Serrano + Show 2 more

Open Access

https://doi.org/10.3390/s20195645

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Abstract

Anomaly detection in the performance of the huge number of elements that are part of cellular networks (base stations, core entities, and user equipment) is one of the most time consuming and key activities for supporting failure management procedures and ensuring the required performance of the telecommunication services. This activity originally relied on direct human inspection of cellular metrics (counters, key performance indicators, etc.). Currently, degradation detection procedures have experienced an evolution towards the use of automatic mechanisms of statistical analysis and machine learning. However, pre-existent solutions typically rely on the manual definition of the values to be considered abnormal or on large sets of labeled data, highly reducing their performance in the presence of long-term trends in the metrics or previously unknown patterns of degradation. In this field, the present work proposes a novel application of transform-based analysis, using wavelet transform, for the detection and study of network degradations. The proposed system is tested using cell-level metrics obtained from a real-world LTE cellular network, showing its capabilities to detect and characterize anomalies of different patterns and in the presence of varied temporal trends. This is performed without the need for manually establishing normality thresholds and taking advantage of wavelet transform capabilities to separate the metrics in multiple time-frequency components. Our results show how direct statistical analysis of these components allows for a successful detection of anomalies beyond the capabilities of detection of previous methods.

Highlights

The complexity of cellular networks is continuously growing
Metric Statistical Threshold (MST): The level of anomaly of each sample metric x [n] is directly measured by how far it is from its mean values, considering a tolerance associated with its standard deviation [40]
Covering the lack of previous works on the topic, the present work proposes an application of transform-based decomposition for the automatic detection of cellular network failures

Summary

Introduction

The complexity of cellular networks is continuously growing. This complexity increases the costs of the network infrastructure and those of its operation, administration, and management (OAM)activities. The complexity of cellular networks is continuously growing. This complexity increases the costs of the network infrastructure and those of its operation, administration, and management (OAM). The huge number of indicators, counters, alarms, and configuration parameters transform network monitoring into a complicated task. In this field, the concept of self-healing, as part of the self-organizing network (SON). Paradigm [1,2], aims to automate the tasks associated with network failure management, achieving a more reliable service provision with minimum operational costs. Self-healing includes the tasks of the detection of degradations in the network service (familiarly known as problems), diagnosis of the root cause or fault generating the problem, compensation of the degradation, and the recovery of the system to its original state.

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Results

Conclusion