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Wireless Networks
Erschienen in: Wireless Networks 6/2016

01.08.2016

Sequence-based detection of sleeping cell failures in mobile networks

verfasst von: Fedor Chernogorov, Sergey Chernov, Kimmo Brigatti, Tapani Ristaniemi

Erschienen in: Wireless Networks | Ausgabe 6/2016

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Abstract

This article presents an automatic malfunction detection framework based on data mining approach to analysis of network event sequences. The considered environment is long term evolution (LTE) of Universal Mobile Telecommunications System with sleeping cell caused by random access channel failure. Sleeping cell problem means unavailability of network service without triggered alarm. The proposed detection framework uses N-gram analysis for identification of abnormal behavior in sequences of network events. These events are collected with minimization of drive tests functionality standardized in LTE. Further processing applies dimensionality reduction, anomaly detection with K-Nearest Neighbors, cross-validation, postprocessing techniques and efficiency evaluation. Different anomaly detection approaches proposed in this paper are compared against each other with both classic data mining metrics, such as F-score and receiver operating characteristic curves, and a newly proposed heuristic approach. Achieved results demonstrate that the suggested method can be used in modern performance monitoring systems for reliable, timely and automatic detection of random access channel sleeping cells.
Vorheriger Artikel Design and operation of energy efficient heterogeneous mobile networks
Nächster Artikel A survey on developing publish/subscribe middleware over wireless sensor/actuator networks

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Fußnoten
1
Evolved Universal Terrestrial Radio Access Network (E-UTRAN)
 
Literatur
1.
Amirijoo, M., Frenger, P., Gunnarsson, F., Moe, J., & Zetterberg, K. (2009). On self-optimization of the random access procedure in 3g long term evolution. In Integrated network management-workshops, 2009. IM ’09. IFIP/IEEE international symposium on, pp 177–184.
2.
Angiulli, F., & Pizzuti, C. (2002). Fast outlier detection in high dimensional spaces. In Proceedings of the 6th European conference on principles of data mining and knowledge discovery, Springer-Verlag, London, UK, UK, PKDD ’02, pp 15–26.
3.
Barco, R., Lazaro, P., Diez, L., & Wille, V. (2008). Continuous versus discrete model in autodiagnosis systems for wireless networks. Mobile Computing, IEEE Transactions on, 7(6), 673–681. doi:10.​1109/​TMC.​2008.​23.CrossRef
4.
Barco, R., Lazaro, P., Wille, V., Diez, L., & Patel, S. (2009). Knowledge acquisition for diagnosis model in wireless networks. Expert Systems with Applications, 36(3, Part 1), 4745–4752.CrossRef
5.
6.
Brown, P. F., deSouza, P. V., Mercer, R. L., Pietra, V. J. D., & Lai, J. C. (1992). Class-based n-gram models of natural language. Computational Linguistics, 18, 467–479.
7.
Brueninghaus, K., Astely, D., Salzer, T., Visuri, S., Alexiou, A., Karger, S., & Seraji, G. A. (2005). Link performance models for system level simulations of broadband radio access systems. In IEEE 16th international symposium on personal, indoor and mobile radio communications, 2005. PIMRC 2005., vol 4, pp 2306–2311 Vol. 4, doi:10.​1109/​PIMRC.​2005.​1651855.
8.
Cavnar, W. B., & Trenkle, J. M. (1994). N-gram-based text categorization. In Proceedings of SDAIR-94, 3rd annual symposium on document analysis and information Retrieval, pp 161–175.
9.
Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Survey, 41(3), 15:1–15:58.CrossRef
10.
Chernogorov, F. (2010). Detection of sleeping cells in long term evolution mobile networks. Master’s thesis, University of Jyväskylä, Finland.
11.
Chernogorov, F., Turkka, J., Ristaniemi, T., & Averbuch, A. (2011). Detection of sleeping cells in LTE networks using diffusion maps. In Vehicular technology conference (VTC Spring), 2011 IEEE 73rd, pp 1–5.
12.
Chernogorov, F., Brigatti, K., Ristaniemi, T., & Chernov, S. (2013). N-gram analysis for sleeping cell detection in LTE networks. In Proceedings of the 38th international conference on acoustics, speech, and signal processing (ICASSP).
13.
Cheung, B., Kumar, G. N., & Rao, S. A. (2005). Statistical algorithms in fault detection and prediction: Toward a healthier network. Bell Labs Technical Journal, 9(4), 171–185.CrossRef
14.
Cheung, B., Fishkin, S. G., Kumar, G. N., & Rao, S.A. (2006a). Method of monitoring wireless network performance. Tech. rep., Los Angeles, CA, uS Patent 2006/0063521 A1, CN1753541A, EP1638253A1.
15.
Cheung, B., Fishkin, S. G., Kumar G.N., & Rao, S. A. (2006b). Method of monitoring wireless network performance. US Patent 2006/0063521 A1, CN1753541A, EP1638253A1
16.
Choi, J., Kim, H., Choi, C., & Kim, P. (2011). Efficient malicious code detection using n-gram analysis and svm. In L. Barolli, F. Xhafa, & M. Takizawa (Eds.), NBiS (pp. 618–621).  : IEEE Computer Society.
17.
Ciocarlie, G., Lindqvist, U., Novaczki, S., & Sanneck, H. (2013). Detecting anomalies in cellular networks using an ensemble method. In Network and service management (CNSM), 2013 9th international conference on, pp 171–174, doi:10.​1109/​CNSM.​2013.​6727831.
18.
Ciocarlie, G., Cheng, C. C., Connolly, C., Lindqvist, U., Nitz, K., Novaczki, S., Sanneck, H., & Naseer-ul Islam, M. (2014a). Anomaly detection and diagnosis for automatic radio network verification. In 6th international conference on mobile networks and management, MONAMI 2014.
19.
Ciocarlie, G., Cheng, C. C., Connolly, C., Lindqvist, U., Novaczki, S., Sanneck, H., & Naseer-ul Islam, M. (2014b). Managing scope changes for cellular network-level anomaly detection. In Wireless communications systems (ISWCS), 2014 11th international symposium on, pp 375–379, doi:10.​1109/​ISWCS.​2014.​6933381.
20.
Ciocarlie, G., Lindqvist, U., Nitz, K., Novaczki, S., & Sanneck, H. (2014c). On the feasibility of deploying cell anomaly detection in operational cellular networks. In Network operations and management symposium (NOMS), 2014 IEEE, pp 1–6, doi:10.​1109/​NOMS.​2014.​6838305.
21.
22.
23.
David, G. (2009). Anomaly detection and classification via diffusion processes in hyper-networks. PhD thesis, Tel-Aviv University, Tel-Aviv, Israel.
24.
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., & Widener, T. (1996). The kdd process for extracting useful knowledge from volumes of data. Communications of the ACM, 39, 27–34.CrossRef
25.
Federal Communications Commission (2011) Small entity compliance guide: Wireless E911 location accuracy requirements. Federal communications commission: Report and order FCC 10-176 PS docket No 07-114 p 3
26.
Ganapathiraju, M., Weisser, D., Rosenfeld, R., Carbonell, J., Reddy, R., & Klein-Seetharaman, J. (2002). Comparative n-gram analysis of whole-genome protein sequences. In Proceedings of the second international conference on Human Language Technology Research, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, HLT ’02, pp 76–81.
27.
3rd Generation Partnership Project. (2009a). Evolved universal terrestrial radio access network (e-utran); self-configuring and self-optimizing network (SON) use cases and solutions (release 9). Tech. Rep. TR 36.902, 3GPP
28.
3rd Generation Partnership Project. (2009b). Technical specification group radio access network; study on minimization of drive-tests in next generation networks (release 9). Tech. Rep. TR 36.805, 3GPP
29.
3rd Generation Partnership Project. (2010). 3GPP; TSG radio access network; further advancements for e-utra physical layer aspects (release 9). Tech. Rep. TR 36.814, 3GPP
30.
3rd Generation Partnership Project. (2011). Technical specification group radio access network; evolved universal terrestrial radio access (e-utra); radio resource control (rrc); protocol specification (release 10). Tech. Rep. TS 36.331, 3GPP
31.
3rd Generation Partnership Project. (2012). Technical report 3rd generation partnership project; technical specification group radio access network; evolved universal terrestrial radio access (e-utra); mobility enhancements in heterogeneous networks (release 11). Tech. rep., 3GPP
32.
3rd Generation Partnership Project. (2014). Self-organizing networks (SON); self-healing concepts and requirements (release 12). Tech. rep., 3GPP TS 32.541 V12.0.0
33.
34.
Guillet, F., & Hamilton, H. J. (Eds.). (2007). Quality measures in data mining, studies in computational intelligence (Vol. 43). Berlin: Springer.MATH
35.
Haidar, M., & O’Shaughnessy, D. (2012). Topic n-gram count language model adaptation for speech recognition. In Spoken language technology workshop (SLT), 2012 IEEE, pp 165–169, doi:10.​1109/​SLT.​2012.​6424216.
36.
Hämälainen, S., Sanneck, H., & Sartori, C. (2012). LTE self-organising networks (SON): Network management automation for operational efficiency (1st ed.). Hoboken: Wiley Publishing.
37.
Han, J., & Kamber, M. (2006). Data mining: Concepts and techniques (2nd ed., Vol. 54). Burlington: Morgan Kaufmann.MATH
38.
Hand, D. J., Smyth, P., & Mannila, H. (2001). Principles of data mining. Cambridge, MA, USA: MIT Press.
39.
Hapsari, W., Umesh, A., Iwamura, M., Tomala, M., Gyula, B., & Sebire, B. (2012a). Minimization of drive tests solution in 3GPP. Communications Magazine, IEEE, 50(6), 28–36.CrossRef
40.
Hapsari, W., Umesh, A., Iwamura, M., Tomala, M., Gyula, B., & Sebire, B. (2012b). Minimization of drive tests solution in 3GPP. Communications Magazine, IEEE, 50(6), 28–36.CrossRef
41.
42.
He, Z., Cichocki, A., & Xie, S. (2009). Efficient method for tucker3 model selection. Electronics Letters, 45, 805.CrossRef
43.
He, Z., Cichocki, A., Xie, S., & Choi, K. (2010). Detecting the number of clusters in n-way probabilistic clustering. IEEE Transaction Pattern Analysis Machine Intelligence, 32(11), 2006–2021.CrossRef
44.
Holma, H., & Toskala, A. (2011). LTE for UMTS: Evolution to LTE-advanced (2nd ed.). Hoboken: Wiley Publishing.CrossRef
45.
Islam, A., & Inkpen, D. (2009). Real-word spelling correction using Google Web 1t n-gram with backoff. In Natural language processing and knowledge engineering, 2009. NLP-KE 2009. International conference on, pp 1 –8, doi:10.​1109/​NLPKE.​2009.​5313823
46.
Johansson, J., Hapsari, W., Kelley, S., & Bodog, G. (2012). Minimization of drive tests in 3GPP release 11. Communications Magazine, IEEE, 50(11), 36–43.CrossRef
47.
Jolliffe, I. (2002). Principal component analysis. Springer series in statistics. Berlin: Springer.MATH
48.
Kac, M., Kiefer, J., & Wolfowitz, J. (1955). On tests of normality and other tests of goodness of fit based on distance methods. The Annals of Mathematical Statistics, 26(2), 189–211.MathSciNetCrossRefMATH
49.
Kassis, E. (2010). Anomaly-based error detection in base station data. Master’s thesis, Tel-Aviv University, Israel
50.
Kela, P. (2007). Downlink channel quality indication for evolved universal terrestrial radio access network. Master’s thesis, University of Jyväskylä, Finland.
51.
Khanafer, R., Solana, B., Triola, J., Barco, R., Moltsen, L., Altman, Z., et al. (2008). Automated diagnosis for umts networks using bayesian network approach. Vehicular Technology, IEEE Transactions on, 57(4), 2451–2461. doi:10.​1109/​TVT.​2007.​912610.CrossRef
52.
Kolehmainen, N. (2007). Downlink packet scheduling performance in evolved universal terrestrial radio access network. Master’s thesis, University of Jyväskylä, Finland.
53.
Laiho, J., Raivio, K., Lehtimaki, P., Hatonen, K., & Simula, O. (2005). Advanced analysis methods for 3g cellular networks. Wireless Communications, IEEE Transactions on, 4(3), 930–942. doi:10.​1109/​TWC.​2005.​847088.CrossRef
54.
Luo, F. L., Unbehauen, R., & Cichocki, A. (1997). A minor component analysis algorithm. Neural Networks, 10(2), 291–297.CrossRef
55.
Mueller, C. M., Kaschub, M., Blankenhorn, C., & Wanke, S. (2008). A cell outage detection algorithm using neighbor cell list reports. In K. Hummel & J. Sterbenz (Eds.), Self-organizing systems (Vol. 5343, pp. 218–229)., Lecture notes in computer science Berlin Heidelberg: Springer.
56.
Nagao, Makoto, Mori, Shinsuke (1994) A new method of n-gram statistics for large number of n and automatic extraction of words and phrases from large text data of japanese. In: Proceedings of the 15th conference on computational linguistics - volume 1, association for computational linguistics, Stroudsburg, PA, USA, COLING ’94, pp 611–615
57.
58.
59.
60.
Novaczki, S. (2013). An improved anomaly detection and diagnosis framework for mobile network operators. In Design of reliable communication networks (DRCN), 2013 9th international conference on the, pp 234–241.
61.
Novaczki, S., & Szilagyi, P. (2011). Radio channel degradation detection and diagnosis based on statistical analysis. In Vehicular technology conference (VTC Spring), 2011 IEEE 73rd, pp 1–2.
62.
63.
Osseiran, A., Braun, V., Hidekazu, T., Marsch, P., Schotten, H., Tullberg, H., Uusitalo, M., & Schellman, M. (2013). The foundation of the mobile and wireless communications system for 2020 and beyond: Challenges, enablers and technology solutions. In Vehicular technology conference (VTC Spring), 2013 IEEE 77th, pp 1–5, doi:10.​1109/​VTCSpring.​2013.​6692781.
64.
Rabin, N. (2010). Data mining dynamically evolving systems via diffusion methodologies. PhD thesis, Tel-Aviv University, Tel-Aviv, Israel.
65.
Raivio, K., Simula, O., Laiho, J., & Lehtimaki, P. (2003). Analysis of mobile radio access network using the self-organizing map. In Integrated network management, 2003. IFIP/IEEE Eighth international symposium on, pp 439–451, doi:10.​1109/​INM.​2003.​1194197.
66.
Ramaswamy, S., Rastogi, R., & Shim, K. (2000). Efficient algorithms for mining outliers from large data sets. SIGMOD Record, 29(2), 427–438.CrossRef
67.
Ramiro, J., & Hamied, K. (2012). Self-organizing networks (SON): Self-planning, self-optimization and self-healing for GSM, UMTS and LTE (1st ed.). Hoboken: Wiley Publishing.
68.
69.
Sesia, S., Baker, M., & Toufik, I. (2011). LTE - The UMTS long term evolution: From theory to practice. Hoboken: Wiley.CrossRef
70.
Szilagyi, P., & Novaczki, S. (2012). An automatic detection and diagnosis framework for mobile communication systems. IEEE Transactions on Network and Service Management, 9(2), 184–197.CrossRef
71.
Turkka, J., Ristaniemi, T., David, G., & Averbuch, A. (2011). Anomaly detection framework for tracing problems in radio networks. In The 10th international conference on networks, ICN 2011.
72.
Turkka, J., Chernogorov, F., Brigatti, K., Ristaniemi, T., & Lempiäinen, J. (2012). An approach for network outage detection from drive-testing databases. Journal of Computer Networks and Communications.
73.
Yilmaz, O. N. C., Hämälainen, J., & Hämälainen, S. (2011). Self-optimization of random access channel in 3rd generation partnership project long term evolution. Wireless Communications and Mobile Computing, 11(12), 1507–1517.CrossRef
Metadaten
Titel
Sequence-based detection of sleeping cell failures in mobile networks
verfasst von
Fedor Chernogorov
Sergey Chernov
Kimmo Brigatti
Tapani Ristaniemi
Publikationsdatum
01.08.2016
Verlag
Springer US
Erschienen in
Wireless Networks / Ausgabe 6/2016
Print ISSN: 1022-0038
Elektronische ISSN: 1572-8196
DOI
https://doi.org/10.1007/s11276-015-1087-9

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