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article-T-Comm-1-12-2019

ANALYSIS OF CONNECTION RELEASES OVER E-RAB PROTOCOL  OF THE LTE/LTE-A MOBILE NETWORK

Vladimir A. Fadeev, Kazan National Research Technical University named after A.N. Tupolev-KAI, Kazan, Russia, vladimir_fadeev1993@mail.ru

Ksenia A. Korsukova, Kazan National Research Technical University named after A.N. Tupolev-KAI, Kazan, Russia, ksenia.belonogova@yandex.ru

Adel F. Nadeev, Kazan National Research Technical University named after A.N. Tupolev-KAI, Kazan, Russia, nad15@mail.ru

 

Abstract
Currently, an approach based on the analysis of time series is widely used to research the properties of complex systems. In particular, this approach is also used to analyze the performance indicators of the quality of cellular networks of LTE/LTE-A standards. As a rule, quality indicators are divided into technical (QoS – Quality of Service) and user-defined (QoE – Quality of Experience), which consist of a number of key performance indicators (KPI). This paper discusses the characteristics of the percentage of disconnections over the E-RAB protocol of the network of the specified standards of one of the regional cellular operators of the Russian Federation. The main goal of this work is a detailed analysis of the selected parameter from the point of view of statistical data processing, as well as correlation analysis, to identify the internal relationships between network quality parameters, as well as the causes of abnormal work incidents. The results obtained are important for the subsequent analysis of the parameter under consideration, as well as for its forecasting in the short and long term. The experience gained in this work is also important from the point of view of a general understanding of the operation and optimization of LTE/LTE-A networks within the framework of regional management, or the management of network segments within the same time zone. Namely, in terms of the continuity of the provision of services based on the IP protocol. Based on the results, intermediate recommendations are generated on optimizing the selected parameter within the framework of the existing operator resources, which is a demonstration of the importance of this study from the point of view of technical applicability.

Keywords:KPI, RAB (Radio Access Bearer), E-RAB Protocol (E-UTRAN Radio Access Bearer), LTE, LTE-A.

References

1. LTE // 3GPP. URL: https://www.3gpp.org/technologies/keywords-acronyms/98-lte (Downloaded: 22 August 2019).
2. Gomez G., et al. Towards a QoE-driven resource control in LTE and LTE-A networks. Journal of Computer Networks and Communications. 2013. Vol. 2013.
3. LTE-A // 3GPP. URL: https://www.3gpp.org/technologies/keywords-acronyms/98-lte (Downloaded: 22 August 2019).
4. Sesia S., Baker M., Toufik I. (2011). LTE-the UMTS long term evolution: from theory to practice. John Wiley & Sons, pp. 35-36.
5. VoLTE // 3GPP. URL: https://www.3gpp.org/news-events/partners-news/1600-gsa_volte (Downloaded: 22 August 2019).
6. Stefan van der Walt, S. Chris Colbert and Gael Varoquaux. (2011). The NumPy Array: A Structure for Efficient Numerical Computation. Computing in Science & Engineering, 13, pp. 22-30. DOI:10.1109/MCSE.2011.37. (publisher link)
7. Wes McKinney. Data Structures for Statistical Computing in Python. (2010). Proceedings of the 9th Python in Science Conference, pp. 51-56. (publisher link)
8. John D. Hunter. (2007). Matplotlib: A 2D Graphics Environment. Computing in Science & Engineering. No. 9, pp. 90-95, DOI:10.1109/MCSE.2007.55 (publisher link)
9. Fabian Pedregosa, et al. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research. No. 12, pp. 2825-2830. (publisher link)
10. Chabdarov Sh.M., Korobkov A.A. (2018). Probabilistic spectra of random variables: conditions of existence and some properties. Systems of Synchronization, signal formation and processing. 2018. Vol. 9. No.2, pp. 162-169.
11. Gaussian mixture models // Scikit-learn project. URL: https://scikit-learn.org/stable/modules/mixture.html (Downloaded: 22 August 2019).
12. Liu Y., et al. Non-orthogonal multiple access for 5G and beyond //arXiv preprint arXiv:1808.00277. 2018.
13. Garg C., Kalra A., Kalra S. Performance Comparison of OFDMA and SCFDMA in LTE Systems.
14. Guide to Optimizing LTE Service Drops // Huawei. URL: https://www.academia.edu/35360415/Guide-to-Optimizing-LTE-Service-Drops.pdf (Downloaded: 22 August 2019).
15. Lee Y., et al. (2010). Effects of time-to-trigger parameter on handover performance in SON-based LTE systems. 2010 16th Asia-Pacific Conference on Communications (APCC). IEEE, pp. 492-496.
16. Awada A. et al. (2013). A SON-based algorithm for the optimization of inter-RAT handover parameters. IEEE Transactions on Vehicular Technology. Vol. 62. No. 5, pp. 1906-1923.
17. Nagarajan D. R., Thiagarajah S. P., Alias M. Y. (2017). Robust son system with enhanced handover performance system. 2017 IEEE 13th Malaysia International Conference on Communications (MICC). IEEE, pp. 276-281.
18. Grus J. (2015). Data science from scratch: first principles with python. O’Reilly Media, Inc., pp. 199-202.
19. Bruschi R., Burgarella G., & Lago P. (2017, September). A Lightweight Prediction Method for Scalable Analytics of Multi-seasonal KPIs. International Tyrrhenian Workshop on Digital Communication, pp. 61-70. Springer, Cham.
20. Joshi M., Hadi T.H. (2015). A review of network traffic analysis and prediction techniques//arXiv preprint arXiv:1507.05722.
21. Feng H., Shu Y. (2005). Study on network traffic prediction techniques. Wireless Communications, Networking and Mobile Computing. 2005 International Conference on. IEEE. Vol. 2, pp. 1041-1044.
22. Katris C., Daskalaki S. (2015). Comparing forecasting approaches for Internet traffic. Expert Systems with Applications. Vol. 42. No.. 21, pp. 8172-8183.