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Modelling LTE Solved Troubleshooting Cases

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Abstract

Self-Organizing Networks (SON) add automation to the Operation and Maintenance of mobile networks. Self-healing is the SON function that performs automated troubleshooting. Among other functions, self-healing performs automatic diagnosis (or root cause analysis), that is the task of identifying the most probable fault causes in problematic cells. For training the automatic diagnosis functionality based on support-decision systems, supervised learning algorithms usually extract the knowledge from a training set made up from solved troubleshooting cases. However, the lack of these sets of real solved cases is the bottleneck in the design of realistic diagnosis systems. In this paper, the properties of such troubleshooting cases and training sets are studied. Subsequently, a method based on model fitting is proposed to extract a statistical model that can be used to generate vectors that emulate the network behavior in the presence of faults. These emulated vectors can then be used to evaluate novel diagnosis systems. In order to evaluate the feasibility of the proposed approach, an LTE fault dataset has been modeled, based on both the analysis of real cases collected over two months and a network simulator. In addition, the obtained baseline model can be very useful for the research community in the area of automatic diagnosis.

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Notes

  1. http://docs.scipy.org/doc/scipy/reference/stats.html.

Abbreviations

3GPP:

3rd Generation Partnership Project

CQI:

Channel Quality Indicator

DI:

Degraded Interval

DL:

Downlink

EMD:

Entropy Minimization Discretization

eNodeB:

E-UTRAN Node B

ERAB:

E-UTRAN Radio Access Bearer

E-UTRAN:

Evolved Terrestrial Radio Access Network

FLC:

Fuzzy Logic Controller

IRAT:

Inter-Radio Access Technology

KB:

Knowledge Base

KBS:

Knowledge Based Systems

KPI:

Key Performance Indicator

K-S:

Kolmogorov–Smirnov

LTE:

Long Term Evolution

NGMN:

Next Generation Mobile Networks

PDF:

Probability Distribution Function

PI:

Performance Indicator

ROP:

Report Output Period

RSRP:

Reference Signal Received Power

RSRQ:

Reference Signal Received Quality

RSSI:

Received Signal Strength Indicator

SINR:

Signal to Interference plus Noise Ratio

SON:

Self-Organizing Networks

UE:

User Equipment

UL:

Uplink

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Acknowledgements

This work has been partially funded by Optimi-Ericsson, Junta de Andalucía (Consejería de Ciencia, Innovación y Empresa, Agencia IDEA, ref. 59288 and Proyecto de Investigación de Excelencia P12-TIC-2905) and ERDF.

Author information

Authors and Affiliations

  1. E.T.S.I. Telecomunicación, Universidad de Málaga, Málaga, Spain

    Emil J. Khatib, Ana Gómez-Andrades & Raquel Barco

  2. E.T.S.I. Telecomunicación, Ericsson, PBO RA Continuous Analysis, Málaga, Spain

    Inmaculada Serrano

Authors
  1. Emil J. Khatib
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  2. Ana Gómez-Andrades
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  3. Inmaculada Serrano
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  4. Raquel Barco
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Corresponding author

Correspondence to Emil J. Khatib.

Appendices

Appendix 1: Model of the Real Dataset

See Tables 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22 and 23.

Table 11 Model parameters for average RSSI
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Table 12 Model parameters for average CQI
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Table 13 Model parameters for CS fallback rate
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Table 14 Model parameters for average number of active UEs
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Table 15 Model parameters for UL traffic
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Table 16 Model parameters for DL traffic volume
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Table 17 Model parameters for handover success rate
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Table 18 Model parameters for interfreq HO preparation rate
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Table 19 Model parameters for intrafreq HO preparation rate
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Table 20 Model parameters for iRAT rate
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Table 21 Model parameters for number of ERAB attempts
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Table 22 Model parameters for number of CPU overload alarms
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Table 23 Model parameters for number of bad coverage reports
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Appendix 2: Model of the Simulated Dataset

See Tables 24, 25, 26, 27, 28, 29 and 30.

Table 24 Model parameters for retainability
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Table 25 Model parameters for handover success rate
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Table 26 Model parameters for 95 percentile of RSRP
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Table 27 Model parameters for 5 percentile of RSRQ
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Table 28 Model parameters for 95 percentile of SINR
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Table 29 Model parameters for throughput
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Table 30 Model parameters for 95 percentile of distance
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Khatib, E.J., Gómez-Andrades, A., Serrano, I. et al. Modelling LTE Solved Troubleshooting Cases. J Netw Syst Manage 26, 23–50 (2018). https://doi.org/10.1007/s10922-017-9406-3

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  • DOI: https://doi.org/10.1007/s10922-017-9406-3

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