Short-term Availability Forecasting in Small Cell Networks

Small cells play an important bridging role for low-latency and high-speed communication in early 5G era. The short-term availability forecasting in small cell networks based on ensemble neural networks was proposed to improve the wireless network quality and customer experience. In this research, the standard specifications of Broadband Forum data model and 3GPP LTE key performance indicator were the input factor in ensemble neural networks to predict the availability in small cell networks. In addition, the network logs were also collected as the input factor of the forecasting model. In experimental result, the accuracy of validation dataset was more than 90%, and the device’s KPI changes were closely related to the malfunction. The device’s parameters could be adjusted and optimized in advance according to the forecasting result to avoid malfunction occurs.

[1]  Giorgio Valentini,et al.  Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods , 2004, J. Mach. Learn. Res..

[2]  Raquel Barco,et al.  Cell Outage Detection Based on Handover Statistics , 2015, IEEE Communications Letters.

[3]  Mohammad Ashraf,et al.  Conceptual design of proactive SONs based on the Big Data framework for 5G cellular networks: A novel Machine Learning perspective facilitating a shift in the SON paradigm , 2016, 2016 International Conference System Modeling & Advancement in Research Trends (SMART).

[4]  Donghai Guan,et al.  Ensemble Learning and SMOTE Based Fault Diagnosis System in Self-Organizing Cellular Networks , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[5]  Tao Zhang,et al.  A handover statistics based approach for Cell Outage Detection in self-organized Heterogeneous Networks , 2017, 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM).

[6]  Satoshi Nagata,et al.  Trends in small cell enhancements in LTE advanced , 2013, IEEE Communications Magazine.

[7]  András A. Benczúr,et al.  Machine Learning Based Session Drop Prediction in LTE Networks and Its SON Aspects , 2015, 2015 IEEE 81st Vehicular Technology Conference (VTC Spring).

[8]  Ulf Lindqvist,et al.  Detecting anomalies in cellular networks using an ensemble method , 2013, Proceedings of the 9th International Conference on Network and Service Management (CNSM 2013).