Machine learning techniques for spatiotemporal traffic prediction in 5G cellular networks

Abstract

Wireless traffic prediction is vital for network planning and management, enabling real-time decisions and both short- and long-term forecasting. Accurate and efficient techniques improve cellular networks by optimizing resource allocation, adapting to dynamic user behavior, and ensuring high-quality service through pattern recognition in network traffic. This facilitates proactive management, including load balancing and beam coordination. This paper developed eight models for cellular network traffic prediction using the telecom Italia big data challenge dataset, which provides a comprehensive view of urban activities and telecommunications in Milan City. These models include seasonal-AutoRegressive-integrated-moving-average, Facebook-prophet, adaptive-boosting (AdaBoost), extreme-gradient-boosting (XGBoost), Long-short-term memory (LSTM), convolutional-neural-network (CNN), hybrid CNN-LSTM, and ensemble model that combined of the outputs of CNN and LSTM. These models were applied to predict different types of network traffic, namely the Internet, SMS, and call traffic across distinct geographic regions: city center, commercial, residential, and business. Each region exhibited unique temporal traffic patterns influenced by weekdays, weekends, and local activities. These models are evaluated based on performance metrics and computational time. The results demonstrate that the ensemble CNN+LSTM is the most accurate model, achieving R² values of 0.990 for Internet, 0.986 for call, and 0.976 for SMS, followed by the hybrid CNN-LSTM and LSTM models. These models are associated with a high level of computational complexity. Meanwhile, the AdaBoost and XGBoost models obtain practical alternatives for balancing accuracy with computational efficiency. Finally, the ensemble CNN+LSTM surpasses prior research, demonstrating enhanced predictive reliability across all network traffic types.