Analysis of Factors Affecting the Use of the 64QAM Modulation on the Long-Term Evolution Network by Using Random Forest Method

Abstract

Nowadays internet traffic using cellular telecommunication network is increasing very rapidly. Good LTE (Long-Term Evolution) cellular network performance is very important for any telecommunication operator to maintain customer satisfaction. Poor network performance can also cause customers to switch to other operators. One of the indicator variables in observing the radio quality of the LTE cellular network is Penetration using 64QAM Modulation. 64QAM modulation can transmit higher bitrates with lower power usage. 64QAM modulation will be used if the Channel Quality Index (CQI) condition is very good. Network quality improvement can be done by adding new BTS or optimizing existing BTS. The addition of new BTS will increase coverage, quality, and capacity but cost is high, and the time required to build BTS is also long, while improving network quality by optimizing BTS can be done by purchasing LTE features and costs incurred still relatively low. In increasing the penetration of using 64QAM modulation, it is necessary to analyze the other variables. The traditional method to improve this Key Performance Indicator (KPI) requires an expert and professional but is often inaccurate and spends a lot of time finding the factors that cause it. To solve this problem, Random forest method is proposed. By knowing the variables that have a significant effect on network quality, the capital costs incurred by cellular operators for improving network quality will be more effective and efficient because the capital costs invested only focus on influencing variables such as purchasing LTE network features only done for those related to these variables. The results of this study, we make CQI improvement flow based on the classification of the random forest method that produces feature/variable importance.