Automatic Feature Selection Technique for Next Generation Self-Organizing Networks

Palacios, David; de-la-Bandera, Isabel; Gómez-Andrades, Ana; Flores, Lydia; Barco, Raquel

doi:10.1109/lcomm.2018.2825392

Cited by 11 publications

(7 citation statements)

References 6 publications

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“…The proposed algorithm was compared with a fuzzy logic approach providing different results depending on the network elements involved. Palacios et al [23] and Hahn et al [24] also proposed methods based on SON. The authors in [23] proposed an automatic selection of KPIs algorithm based on the overlapping area of the probability density function.…”

Section: Root Cause Analysis (Rca) In Telecommunicationmentioning

confidence: 99%

“…Palacios et al [23] and Hahn et al [24] also proposed methods based on SON. The authors in [23] proposed an automatic selection of KPIs algorithm based on the overlapping area of the probability density function. This allowed analysis of statistical behaviour of the network states and performance indicators.…”

Section: Root Cause Analysis (Rca) In Telecommunicationmentioning

confidence: 99%

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A quadri-dimensional approach for poor performance prioritization in mobile networks using Big Data

Mampaka

Sumbwanyambe

2019

J Big Data

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IntroductionCommunication service providers (CSPs) have reached the ceiling in terms of new customer acquisitions. Therefore, acquiring new customers is much difficult than it is for existing customers to churn. Traditional network operation centres (NOC) have been very inefficient in terms of problem finding, handling and resolution. Within this ambit, and driven by the need for fast service, the NOC approach of managing network incidents has changed to the new paradigm of service quality management (SQM) and customer experience management (CEM). This requires mobile network operators to be more service-oriented and customer-oriented by using the service operation centre (SOC) approach.While the traditional approach of mobile network monitoring follows a bottom-up approach, i.e. Starting with the network elements management, network alarming and quality of service (QoS) issues through historical key performance indicators (KPI) AbstractThe Management of mobile networks has become so complex due to a huge number of devices, technologies and services involved. Network optimization and incidents management in mobile networks determine the level of the quality of service provided by the communication service providers (CSPs). Generally, the down time of a system and the time taken to repair [mean time to repair (MTTR)] has a direct impact on the revenue, especially on the operational expenditure (OPEX). A fast root cause analysis (RCA) mechanism is therefore crucial to improve the efficiency of the operational team within the CSPs. This paper proposes a quadri-dimensional approach (i.e. services, subscribers, handsets and cells) to build a service quality management (SQM) tree in a Big Data platform. This is meant to speed up the root cause analysis and prioritize the elements impacting the performance of the network. Two algorithms have been proposed; the first one, to normalize the performance indicators and the second one to build the SQM tree by aggregating the performance indicators for different dimensions to allow ranking and detection of tree paths with the worst performance. Additionally, the proposed approach will allow CSPs to detect the mobile network dimensions causing network issues in a faster way and protect their revenue while improving the quality of the service delivered. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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Section: Root Cause Analysis (Rca) In Telecommunicationmentioning

confidence: 99%

Section: Root Cause Analysis (Rca) In Telecommunicationmentioning

confidence: 99%

A quadri-dimensional approach for poor performance prioritization in mobile networks using Big Data

Mampaka

Sumbwanyambe

2019

J Big Data

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“…This is why log messages are usually pre-processed before being scrutinized by human experts who are in charge of identifying and mitigating the issues by appropriate actions. This analysis, while partly automated and aided by ad-hoc tools [1], is often time consuming and inefficient. Network operators would like to increase the automation of this analysis, in particular for cellular networks, in order to reduce the time needed to detect, and fix, performance issues and to spot more complicated cases that are not always detected by human operators.…”

Section: Introductionmentioning

confidence: 99%

Introducing an Unsupervised Automated Solution for Root Cause Diagnosis in Mobile Networks

Mdini

Simon

Blanc

et al. 2020

IEEE Trans. Netw. Serv. Manage.

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Today's network operators strive to create self-healing cellular networks that have a fully automated troubleshooting management process. To this end, the network monitoring system should be capable of detecting issues, diagnosing them, and triggering the adequate recovery action. In this paper, we propose an unsupervised solution to diagnose the root causes of network issues. As monitoring systems collect a large number of logs from the different devices in their networks, it is possible to determine which connections resulted in a poor user experience and apply a failed/successful label. Our solution, Automatic Root Cause Diagnosis (ARCD), analyzes labeled connection logs to identify the major contributors to the network inefficiency (e.g., a faulty core device) as well as the incompatibilities between different elements (e.g., make and model of a phone not being able to access a service). We evaluate the effectiveness of our solution by using logs from three different real cellular networks. In each case, ARCD was able to identify the major contributors and the most widespread incompatibilities. In the three cases, the precision (detection accuracy) and the recall (detection rate) are higher than 90%.

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“…In [4], however, authors applied dimensionality reduction to improve the classification of Internet traffic. In [5,6], supervised techniques for automatic feature selection are applied to determine the most useful KPIs to eventually identify the cause for a performance degradation in a cellular network. Specifically, in [5], a supervised technique based on a genetic algorithm is proposed.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, in [5], a supervised technique based on a genetic algorithm is proposed. The authors of [6] also propose a supervised method, which relies on the statistical dissimilarity of KPIs when conditioned to different network states.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction for Dimensionality Reduction in Cellular Networks Performance Analysis

de-la-Bandera

Palacios²,

Mendoza

et al. 2020

Sensors

Self Cite

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Next-generation mobile communications networks will have to cope with an extraordinary amount and variety of network performance indicators, causing an increase in the storage needs of the network databases and the degradation of the management functions due to the high-dimensionality of every network observation. In this paper, different techniques for feature extraction are described and proposed as a means for reducing this high dimensionality, to be integrated as an intermediate stage between the monitoring of the network performance indicators and their usage in mobile networks’ management functions. Results using a dataset gathered from a live cellular network show the benefits of this approach, in terms both of storage savings and subsequent management function improvements.

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Automatic Feature Selection Technique for Next Generation Self-Organizing Networks

Cited by 11 publications

References 6 publications

A quadri-dimensional approach for poor performance prioritization in mobile networks using Big Data

A quadri-dimensional approach for poor performance prioritization in mobile networks using Big Data

Introducing an Unsupervised Automated Solution for Root Cause Diagnosis in Mobile Networks

Feature Extraction for Dimensionality Reduction in Cellular Networks Performance Analysis

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