4G LTE Network Throughput Modelling and Prediction

• Published in: GLOBECOM 2020 - 2020 IEEE Global Communications Conference pp. 1 - 6
• Main Authors: Elsherbiny, Habiba, Abbas, Hazem M., Abou-zeid, Hatem, Hassanein, Hossam S., Noureldin, Aboelmagd
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2020
• Subjects: Analytical models; Cellular Networks; Data models; LTE; Machine learning; Prediction algorithms; Predictive models; Throughput; Throughput Prediction; Time series analysis
• ISSN: 2576-6813
• DOI: 10.1109/GLOBECOM42002.2020.9322410

The past decade has witnessed a staggering evolution in cellular networks. Mobile wireless technologies have undergone four distinct generations; from uncomplicated voice calls in the first generation to high-speed, low latency and video streaming in the fourth generation. The numerous services brought to the users by 4G network have caused an increasing load demand. This increasing demand in network usage has proven the necessity of further service enhancements, such as predictive resource allocation techniques and handover analysis. For these techniques to be deployed, network quality and performance analysis must be performed on real-world network data. Since throughput is a major indicator of the network's performance, throughput modelling and prediction can be utilized for analyzing network quality. In this paper, two approaches for throughput analysis are examined: classical machine learning and time series forecasting. For the first approach, various machine learning models were deployed for throughput prediction and our analysis showed that the random forest model achieved the highest prediction performance. For time series forecasting, statistical methods as well as deep learning architectures were used. The evaluation shows that the machine learning models had a higher throughput prediction performance than the time series forecasting techniques.