CELL OUTAGE DETECTION IN LTE-A CELLULAR SYSTEMS USING NEURAL NETWORKS

Abstract

Self-organizing networks (SONs) are considered as one of the key features for automation of network management in new generation of mobile communications. The upcoming fifth generation (5G) mobile networks are likely to offer new challenges for SON solutions. In SON structure, self-healing is an outstanding task which comes along with Cell Outage Detection (COD) and Cell Outage Compensation (COC). This study investigates the detection of cell outages by means of the metrics generated in the User Equipment (UE) with the help of pattern recognition methods such as Neural Networks, Logistic Regression and k-Means algorithms. Based on the metrics like Signal to Interference Noise Ratio (SINR), Reference Signal Received Quality (RSRQ), and Channel Quality Indicator (CQI), large amount of data is processed with supervised and unsupervised algorithms for the purpose of classifying outages and possible degradations. Our results suggest that in 79.74% of the simulation cases, Neural Network structure was able to identify the correct state of the cells whether it is outage or not with a true positive rate of 87.61% and a true negative rate of 71.87% whereas Logistic Regression gave a success rate of 78.73%, true positive rate of 88.15%, and true negative rate of 69.3%. As a future work, more sophisticated state-of-the-art deep learning mechanisms can lead us to much more successful results in cell outage detection.   

Keywords

• Cell outage detection,neural networks,5G,self-organizing networks

References

1. Amirijoo, M & Jorguseski, Ljupco & Kürner, Thomas & Litjens, Remco & Eden, Michaela & Schmelz, Lars & Turke, U, Cell Outage Management in LTE Networks. Proceedings of the 2009 6th International Symposium on Wireless Communication Systems, ISWCS'09. (2009), 600-604. 10.1109/ ISWCS. 2009.5285232.
2. 3rd Generation Partnership Project; Technical Specification Group Services and System Aspects; Telecommunications Management; Self-Organizing Networks (SON); Self-Healing Concepts and Requirements (Release 11), 3GPP TS 32.541, 2012-09, v11.0.0, 2012.
3. Wang, W., Liao, Q. and Zhang, Q., COD: A cooperative cell outage detection architecture for self-organizing femtocell networks, IEEE Trans. Wireless Commun., vol. 13, no. 11, , (Nov. 2014), pp. 6007–6014.
4. De la Bandera, I., Barco, R., Munoz, P. and Serrano, I., Cell outage detection based on handover statistics, IEEE Commun. Lett., vol. 19, no. 7, (Jul. 2015), pp. 1189–1192.
5. Mueller, C., Kaschub, M., Blankenhorn, C. and Wanke, S., A cell outage detection algorithm using neighbor cell list reports, Proc. Int. Workshop Self-Organizing Syst., (2008), pp. 218–229.
6. Liao, Q., Wiczanowski, M. and Stanczak, S., Toward cell outage detection with composite hypothesis testing, Proc. IEEE ICC, Jun. 2012, pp. 4883–4887.
7. Ma, Y., Peng, M., Xue, W. and Ji, X., A dynamic affinity propagation clustering algorithm for cell outage detection in self-healing networks, Proc. IEEE WCNC, (Apr. 2013), pp. 2266–2270.
8. Khanafer, R. et al., Automated diagnosis for UMTS networks using Bayesian network approach, IEEE Trans. Veh. Technol., vol. 57, no. 4, (Jul. 2008), pp. 2451–2461.

9. Alias, M., Saxena, N. and Roy, A., Efficient Cell Outage Detection in 5G HetNets Using Hidden Markov Model, IEEE Communications Letters, vol. 20, no. 3, (March 2016), pp. 562-565. [ Onireti, O. et al., A Cell Outage Management Framework for Dense Heterogeneous Networks, IEEE Transactions on Vehicular Technology, vol. 65, no. 4, (April 2016), pp. 2097-2113. [ Bodrog, L., Kajo, M., Kocsis S. and Schultz, B., A robust algorithm for anomaly detection in mobile networks, 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Valencia, (2016), pp. 1-6.
10. Ikuno, J. C., Wrulich, M. and Rupp, M., System Level Simulation of LTE Networks, 2010 IEEE 71st Vehicular Technology Conference, Taipei, (2010), pp. 1-5.
11. Cortes, C., Jackel, L. D., Solla, S. A., Vapnik, V., and Denker, J. S., Learning curves: Asymptotic values and rate of convergence, In Cowan, J. D., Tesauro, G., and Alspector, J., editors, Morgan Kaufmann Publishers Inc., NIPS volume 6, (1994), 327–334.