Automatic root cause analysis based on traces for LTE self-organizing networks

Gómez-Andrades, Ana; Barco, Raquel; Serrano, Immaculada; Delgado, Patricia; Caro-Oliver, Patricia; Muñoz, Pablo

doi:10.1109/mwc.2016.7498071

Cited by 17 publications

(9 citation statements)

References 10 publications

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“…The system supports rich and sophisticated analysis tasks based on time-evolving graphs of traffic. With a more limited scope, References [45][46][47] present new performance indicators obtained from events in connection traces for self-tuning and self-healing LTE cellular networks. Nonetheless, to the authors' knowledge, none of these studies has applied CEP to improve the automatic optimization of cellular networks by taking advantage of the flexibility to generate new performance counters from network data.…”

Section: Related Workmentioning

confidence: 99%

Complex Event Processing for Self-Optimizing Cellular Networks

de-la-Bandera

Toril

Luna-Ramírez

et al. 2020

Sensors

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In a cellular network, signaling and data messages exchanged between network elements are an extremely valuable information for network optimization. The consideration of different types of information allows to improve the optimization results. However, the huge amount of information has made it very difficult for operators to process all the available information. To cope with this issue, in this paper, a methodology for processing cell and user connection traces to optimize a live cellular network is presented. The aim is to generate new performance indicators different from those supplied by manufacturers, taking advantage of the ability of complex event processing tools to correlate events of different nature. For illustrative purposes, an example of how a new performance indicator is created from real traces by complex event processing is given.

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Section: Related Workmentioning

confidence: 99%

Complex Event Processing for Self-Optimizing Cellular Networks

de-la-Bandera

Toril

Luna-Ramírez

et al. 2020

Sensors

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“…The work in [8] whose aim is to diagnose problems at the cell level from traditional KPIs has been further investigated in [37] by employing call traces (as opposed to traditional KPIs) and applying a rule-based system to them. That work has been extended in [38], where a method based on Neural Networks (similar to that in [8]), is applied to diagnose the users.…”

Section: Related Workmentioning

confidence: 99%

“…Then, from such a user diagnosis, a threshold-based method is applied to diagnose faulty cells. Due to its benefits, the present work also employs call traces as in [37,38], instead of traditional KPIs. However, the proposed method improves the results presented in [38], whose method for cell diagnosis shows a clear dependence on thresholds settings.…”

Section: Related Workmentioning

confidence: 99%

“…Unlike [38], the proposed method in this paper for cell diagnosis is based on the correlation to specific cell profiles so that the use of thresholds is avoided. In addition, the work in [37,38] focuses on connections whose release has been abnormal, which can be considered a limitation in the case of data traffic, where the QoS evaluation during the session in both uplink and downlink is essential for diagnosis purposes.…”

Section: Related Workmentioning

confidence: 99%

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A method for identifying faulty cells using a classification tree-based UE diagnosis in LTE

Muñoz

Barco

Cruz

et al. 2017

J Wireless Com Network

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The latest advances in wireless technologies have led to a proliferation of data mobile devices and services. As a consequence, mobile networks have experienced a significant increase in data traffic, while voice traffic has shown nearly no growth. It is therefore essential for operators to understand the data traffic behavior at the user level in order to ensure a good customer experience. In the radio access networks (RANs), traditional solutions based on cell-level measurements are not adequate to analyze performance of individual users. Instead, novel alternatives such as the use of call traces and the definition of new user-centric indicators will provide detailed and valuable information for each connection. One of the key measurements related to data services is the user throughput. In this work, the user throughput is adopted as the main attribute to conduct diagnosis in the RAN, which has typically been the bottleneck for data services. To that end, a binary classification tree is proposed to determine the root cause of poor throughput in user-level data sessions. Then, this information is aggregated at the cell level in order to provide effective diagnosis of degraded cells. In particular, a correlation-based analysis of the cell status is proposed in order to identify abnormal cell behaviors in an automatic way. Evaluation has been carried out with datasets from live cellular networks. Results show that the proposed diagnosis approach is an effective means to identify the main factors that limit the user throughput in network cells.

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“…In the context of SON, several works have considered the use of traces to improve self-healing and self-optimization algorithms. In [20], a root cause analysis methodology based on user connection traces is proposed to identify the cause of abnormal connection releases in a LTE system. In [21], a self-tuning algorithm is proposed for adjusting antenna tilts in a LTE system on a cell-by-cell basis.…”

Section: Related Workmentioning

confidence: 99%

A Trace Data-Based Approach for an Accurate Estimation of Precise Utilization Maps in LTE

Sánchez

Acedo-Hernández

Toril

et al. 2017

Mobile Information Systems

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For network planning and optimization purposes, mobile operators make use of Key Performance Indicators (KPIs), computed from Performance Measurements (PMs), to determine whether network performance needs to be improved. In current networks, PMs, and therefore KPIs, suffer from lack of precision due to an insufficient temporal and/or spatial granularity. In this work, an automatic method, based on data traces, is proposed to improve the accuracy of radio network utilization measurements collected in a Long-Term Evolution (LTE) network. The method’s output is an accurate estimate of the spatial and temporal distribution for the cell utilization ratio that can be extended to other indicators. The method can be used to improve automatic network planning and optimization algorithms in a centralized Self-Organizing Network (SON) entity, since potential issues can be more precisely detected and located inside a cell thanks to temporal and spatial precision. The proposed method is tested with real connection traces gathered in a large geographical area of a live LTE network and considers overload problems due to trace file size limitations, which is a key consideration when analysing a large network. Results show how these distributions provide a very detailed information of network utilization, compared to cell based statistics.

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Automatic root cause analysis based on traces for LTE self-organizing networks

Cited by 17 publications

References 10 publications

Complex Event Processing for Self-Optimizing Cellular Networks

Complex Event Processing for Self-Optimizing Cellular Networks

A method for identifying faulty cells using a classification tree-based UE diagnosis in LTE

A Trace Data-Based Approach for an Accurate Estimation of Precise Utilization Maps in LTE

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