Open AccessMONITORING SYSTEM FOR LTE-A CELLULAR COMMUNICATION NETWORK ACCESSIBILITY INDICATORS
Author(s) -
Vladimir Fadeev,
Shaikhrozy Zaidullin,
Zlata Fadeeva,
Adel Nadeev
Publication year - 2021
Publication title -
t-comm
Language(s) - English
Resource type - Journals
eISSN - 2072-8743
pISSN - 2072-8735
DOI - 10.36724/2072-8735-2021-15-3-4-16
Subject(s) - exponential smoothing , computer science , software deployment , smoothing , process (computing) , decomposition , series (stratigraphy) , data mining , real time computing , ecology , paleontology , computer vision , biology , operating system
In this paper we consider two accessibility indicators, namely E-RAB (E-UTRAN Radio Access Bearer) and E-RRC (Evolved Radio Resource Control) failure rates, of the LTE-A communication network belonging to one of the regional operators in Russian Federation. The aim of this study is to find the proper algorithms for accessibility indicators prediction, and performance estimation of these algorithms. During the study, we provide temporal dynamics of the indicators and possible failure reasons, behind these indicators. Then the percentage of the time series values is shown, which are corresponding to the abnormal situations (incidents). After that, the stationarity of the inspected time series using augmented Dickey-Fuller (ADFuller), and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) methods is analyzed. Next, the ETS decomposition is performed. In order to predict the future values of the indicators, we utilize SARIMA model, triple Exponential Smoothing (Holt-Winters method), Facebook Prophet, Prony decomposition based model and XGBoost algorithm. Performance estimation is obtained in two ways: by test-sequence- and cross-validation-based Median Absolute Error (MAE). Also, the architecture for the monitoring system, that collects, analyzes and visualizes the required metrics within the infrastructure of the considered operator, is proposed in this paper. Herein, we analyze the possibilities of the open-source solution deployment on each stage of the monitoring process from data mining and preparation up to predictive model learning.