Location-based distributed sleeping cell detection and root cause analysis for 5G ultra-dense networks

Fortes, Sergio; Barco, Raquel; Aguilar-Garcı́a, Alejandro

doi:10.1186/s13638-016-0649-6

Cited by 15 publications

(11 citation statements)

References 24 publications

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“…Machine learning classifier techniques, such as naive Bayesian classifiers [ 4 ], k-nearest neighbor, or support vector machine [ 18 ], can also be implemented in a binary way to identify values as degraded or not. Where these techniques automatically integrate the detection decision, their need for labeled cases and typically their analysis of the metric values in an atemporal manner can make them often underperform.…”

Section: Related Workmentioning

confidence: 99%

“…In cellular networks, failure detection is based on the analysis of network metrics (indicators, key performance indicators (KPIs), counters, alarms, and mobile traces) by means of identifying abnormal/unhealthy values in its temporal series. Classic techniques for cell degradation detection are based on the definition of threshold values (upper or lower) [ 3 , 4 , 5 ]. If the values for a given metric violate such thresholds, it is considered degraded.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Transform-Based Multiresolution Decomposition for Degradation Detection in Cellular Networks

Fortes

Muñoz

Serrano

et al. 2020

Sensors

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“…The recent advances in indoor localization and UE data are utilized to provide sleeping cell detection and diagnosis solutions for 5G ultra-dense networks in [23]. An automatic root-cause analysis method using UE traces is presented in [24].…”

Section: User Measurements In Traditional Shmentioning

confidence: 99%

“…More recently, e.g., in [23], further kinds of data, including user context, are proposed to be considered. Context information can be broadly collected from the following three major sources:…”

Section: Context-awarenessmentioning

confidence: 99%

Towards proactive context-aware self-healing for 5G networks

et al. 2017

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In this paper, we suggest a new research direction and a future vision for Self-Healing (SH) in Self-Organizing Networks (SONs). The problem we wish to solve is that traditional SH solutions may not be sufficient for the future needs of cellular network management because of their reactive nature, i.e., they start recovering after detecting already occurred faults instead of preparing for possible future faults in a pre-emptive manner. The detection delays are especially problematic with regard to the zero latency requirements of 5G networks. To address this problem, existing SONs need to be upgraded from reactive to proactive response. One of the dimensions in SH research is to employ more holistic context information that includes, e.g., user location and mobility information, in addition to traditional context information mostly gathered from sources inside the network. Such extra information has already been found useful in SH. In this paper, we suggest how user context information can not only be incorporated in SH but also how future context could be predicted based on currently available information. We present a user mobility case study as an example to illustrate our idea.

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“…Although this and the posterior work in [ 20 ] have shown the capabilities of such indicators for the detection and diagnosis of cellular failures, they did not address some of the main challenges introduced by the contextualized indicators and that are common to most other approaches for context and location awareness. Firstly, the use of context and, particularly, location, creates new metrics/features (e.g., contextualized indicators) that are unknown by current cellular engineers and staff, which makes them extremely difficult to be “manually” defined and for their calculation and properly chosen for their application as inputs of inference rules.…”

Section: Introductionmentioning

confidence: 96%

Location-Awareness for Failure Management in Cellular Networks: An Integrated Approach

Fortes

Baena

Villegas

et al. 2021

Sensors

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Recent years have seen the proliferation of different techniques for outdoor and, especially, indoor positioning. Still being a field in development, localization is expected to be fully pervasive in the next few years. Although the development of such techniques is driven by the commercialization of location-based services (e.g., navigation), its application to support cellular management is considered to be a key approach for improving its resilience and performance. When different approaches have been defined for integrating location information into the failure management activities, they commonly ignore the increase in the dimensionality of the data as well as their integration into the complete flow of networks failure management. Taking this into account, the present work proposes a complete integrated approach for location-aware failure management, covering the gathering of network and positioning data, the generation of metrics, the reduction in the dimensionality of such data, and the application of inference mechanisms. The proposed scheme is then evaluated by system-level simulation in ultra-dense scenarios, showing the capabilities of the approach to increase the reliability of the supported diagnosis process as well as reducing its computational cost.

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Location-based distributed sleeping cell detection and root cause analysis for 5G ultra-dense networks

Cited by 15 publications

References 24 publications

Transform-Based Multiresolution Decomposition for Degradation Detection in Cellular Networks

Transform-Based Multiresolution Decomposition for Degradation Detection in Cellular Networks

Towards proactive context-aware self-healing for 5G networks

Location-Awareness for Failure Management in Cellular Networks: An Integrated Approach

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