A data-mining framework for analyzing a cellular network drive testing database is described in this paper. The presented method is designed to detect sleeping base stations, network outage, and change of the dominance areas in a cognitive and self-organizing manner. The essence of the method is to find similarities between periodical network measurements and previously known outage data. For this purpose, diffusion maps dimensionality reduction and nearest neighbor data classification methods are utilized. The method is cognitive because it requires training data for the outage detection. In addition, the method is autonomous because it uses minimization of drive testing (MDT) functionality to gather the training and testing data. Motivation of classifying MDT measurement reports to periodical, handover, and outage categories is to detect areas where periodical reports start to become similar to the outage samples. Moreover, these areas are associated with estimated dominance areas to detected sleeping base stations. In the studied verification case, measurement classification results in an increase of the amount of samples which can be used for detection of performance degradations, and consequently, makes the outage detection faster and more reliable. 1. Introduction Modern radio access networks (RAN) are complex infrastructures consisting of several overlaying and cooperating networks such as next-generation high-speed-packet-access (HSPA) and long-term evolution (LTE) networks and as such are prone to the impacts of uncertainty on system management and stability. Classical network management is based on a design principle which requires knowledge of the state of all existing entities within the network at all times. This approach has been successfully applied to networks of limited scale but it is foreseen to be insufficient in the management of future complex networks. In order to maintain a massive multivendor and multi-RAN infrastructure in a cost-efficient manner, operators have to employ automated solutions to optimize the most difficult and time-consuming network operation procedures. Self-organizing network concept [1] has emerged in the last years, with the goal to foster automation and to reduce human involvement in management tasks. It implies autonomous configuration, optimization, and healing actions which would result in a reduced operational burden and improve the experienced end user quality-of-service (QoS). One of the downsides of the SON concept is the necessity to gather larger amounts of operational data from user equipment (UE)
References
[1]
3GPP TS 36.902, “Evolved Universal Terrestrial Radio Access Network (E-UTRAN), Self-Configuring and Self-Optimizing Network (SON) Use Cases and Solutions,” v.9.3.1, March 2011.
[2]
NGMN Alliance, “Next Generation Mobile Networks Use Cases related to Self Organising Network, Overall Description,” v.2.02, December 2008.
[3]
3GPP TR 36.805, “Study on minimization of drive-tests in Next Generation Networks,” v.9.0.0, December, 2009.
[4]
3GPP TS 37.320, “Radio measurement collection for Minimization of Drive Tests,” v.0.7.0, June 2010.
[5]
3GPP RP-111361, “Enhancement of Minimization of Drive Tests for E-UTRAN and UTRAN—Core Part Approval,” Nokia Siemens Networks, Nokia, MediaTek, 2011.
[6]
3GPP TS 32.422, “Subscriber and equipment trace, Trace control and configuration management,” v.11.0.1, September 2011.
[7]
3GPP TS 36.331, “Evolved Universal Terrestrial Radio Access (E-UTRA), Radio Resource Control (RRC), Protocol specification,” v.10.4.0, December 2011.
[8]
M. Amirijoo, L. Jorguseski, T. Kürner et al., “Cell outage management in LTE networks,” in Proceedings of the 6th International Symposium on Wireless Communication Systems (ISWCS '09), pp. 600–604, Siena, Italy, September 2009.
[9]
M. Amirijoo, L. Jorguseski, R. Litjens, and R. Nascimento, “Effectiveness of cell outage compensation in LTE networks,” in Proceedings of the IEEE Consumer Communications and Networking Conference (CCNC '11), pp. 642–647, Las Vegas, Nev, USA, January 2011.
[10]
J. Han and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2000.
[11]
S. B. Kotsiantis, D. Kanellopoulus, and P. E. Pintelas, “Data Preprocessing for Supervised Learning,” International Journal of Computer Science, vol. 1, no. 2, pp. 111–117, 2006.
[12]
F. Chernogorov, J. Turkka, T. Ristaniemi, and A. Averbuch, Detection of Sleeping Cells in LTE Networks Using Diffusion Maps, VTC Spring, Budapest, Hungary, 2011.
[13]
C. M. Mueller, M. Kaschub, C. Blankenhorn, and S. Wanke, “A cell outage detection algorithm using neighbor cell list reports,” in Proceedings of the International Workshop on Self-Organizing Systems, pp. 218–229, 2008.
[14]
3GPP TS 36.214, “Evolved Universal Terrestrial Radio Access (E-UTRA); Physical Layer; Measurements,” v.10.1.0, March 2011.
[15]
J. Turkka and J. Puttonen, “Using LTE power headroom report for coverage optimization,” in Proceedings of 74th IEEE Vehicular Technology Conference (VTC '11-Fall), San Francisco, Calif, USA, September 2011.
[16]
R. R. Coifman, S. Lafon, A. B. Lee et al., “Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 21, pp. 7426–7431, 2005.
[17]
R. R. Coifman and S. Lafon, “Diffusion maps,” Applied and Computational Harmonic Analysis, vol. 21, no. 1, pp. 5–30, 2006.
[18]
B. Nadler, S. Lafon, and R. R. Coifman, “Diffusion maps, spectral clustering and eigenfunctions of fokker-planck operators,” in Advances Neural Information Processing Systems, vol. 18, pp. 955–962, 2005.
[19]
A. Schclar, “A diffusion framework for dimensionality reduction,” in Soft Computing For Knowledge Discovery and Data Mining, chapter IV, pp. 315–325, 2008.
[20]
3GPP TS 36.814, “Further advancements for E-UTRA physical layer aspects (Release 9),” v.9.0.0, March 2010.
[21]
H. He and E. A. Garcia, “Learning from imbalanced data,” IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 9, pp. 1263–1284, 2009.
[22]
R. C. Holte, L. Acker, and B. W. Porter, “Concept learning and the problem of small disjuncts,” in Proceedings of the International Joint Conference on Artificial Intelligence, pp. 813–818, 1989.
