Anomaly Prevision in Radio Access Networks Using Functional Data Analysis

Slimen, Yosra Ben; Allio, Sylvain; Jacques, Julien

doi:10.1109/glocom.2017.8254087

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…The number of principal components m ≤ M is chosen so that at least 90% of the information is covered. As a result, each curve is defined by a vector of its principal components of size m. For more details, we refer the reader to [18] and [19].…”

Section: Dimension Reductionmentioning

confidence: 99%

Quality of perception prediction in 5G slices for e-Health services using user-perceived QoS

Slimen

Balcerzak

Pages

et al. 2021

Computer Communications

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In order to compete for a prominent market share, network operators and service providers should retain and increase the verticals' subscription, catering to their needs in order to differentiate themselves from competitors. In this scenario, verticals' satisfaction arises of paramount importance. As such, user experience is becoming a reliable indicator for service providers and telecommunication operators to convey overall end-to-end system functioning. To properly estimate end user satisfaction, operators and service providers require efficient means for quality monitoring and estimation at all layers, in conjunction with mechanisms able to maintain said quality at optimum levels. Given these factors, this paper proposes a mechanism for Quality of Perception (QoP) estimation in e-Health services, enabling the QoP-aware management of network slices fulfilling the requirements of supported services. To this end, the paper proposes a cognitive-based architecture which allows for the collection and monitoring of verticals' data to estimate QoP and provides mechanisms to re-configure the underlying network slices according to the monitored quality levels. A machine learning (ML) model is introduced that aims to forecast any future degradation in the quality perceived by vertical users. In case of a predicted degradation, the proposed architecture reacts and triggers the necessary remedial actions, referred as actuations. In order to evaluate the developed ML model and to showcase the

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Section: Dimension Reductionmentioning

confidence: 99%

Quality of perception prediction in 5G slices for e-Health services using user-perceived QoS

Slimen

Balcerzak

Pages

et al. 2021

Computer Communications

Self Cite

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“…The smoother versions are compared to the original ones to detect unexpected behaviors. Again, in the context of abnormal behavior detection, wavelets were also described as applicable for KPI smoothing in [ 30 ]. As mentioned, in both cases, the transforms are used for the smoothing of the metrics, missing the multiresolution information associated with the different trends obtained by transform-based analysis.…”

Section: Related Workmentioning

confidence: 99%

Transform-Based Multiresolution Decomposition for Degradation Detection in Cellular Networks

Fortes

Muñoz

Serrano

et al. 2020

Sensors

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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.

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“…Yu and Lambert [20] analysed records for completed international calls. The objective of the paper by Ben Slimen et al [18] is to detect future malfunctions of a set of cells, by only observing key performance indicators that are considered as functional data. Aspirot et al [2] study a non-parametric regression model, where the explanatory variable is non-stationary dependent functional and the response variable is scalar.…”

Section: Introductionmentioning

confidence: 99%

Functional Modelling of Telecommunications Data

Birbilas

Račkauskas

2022

Mathematical Modelling and Analysis

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This work deals with statistical modeling and forecasting of telecommunications data. Main mobile traffic events (SMS, Voice calls, Mobile data) are smoothed using B-spline functions and later analyzed in a functional framework. Functional linear auto-regression models are fitted using both bottom-up and topdown design methodologies. The advantages and disadvantages of both approaches for the prediction of mobile telephone users’ habits are discussed.

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Anomaly Prevision in Radio Access Networks Using Functional Data Analysis

Cited by 8 publications

References 11 publications

Quality of perception prediction in 5G slices for e-Health services using user-perceived QoS

Quality of perception prediction in 5G slices for e-Health services using user-perceived QoS

Transform-Based Multiresolution Decomposition for Degradation Detection in Cellular Networks

Functional Modelling of Telecommunications Data

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