Machine Learning-Based Traffic Prediction for Intelligent Telecommunication and Iot Networks

Resumen

The rapid proliferation of Internet of Things (IoT) devices and the evolution of intelligent telecommunication networks have significantly increased the complexity and volume of network traffic. Traditional traffic management approaches are often inadequate for handling dynamic traffic patterns, resulting in network congestion, increased latency, packet loss, and inefficient resource utilization. Machine learning (ML) has emerged as a promising solution for accurately predicting network traffic and enabling intelligent network management through proactive decision-making. This review paper presents a comprehensive analysis of machine learning-based traffic prediction techniques for intelligent telecommunication and IoT networks. It critically examines conventional machine learning algorithms, including Linear Regression, Support Vector Machines (SVM), Decision Trees, Random Forest, and Extreme Gradient Boosting (XGBoost), alongside advanced deep learning models such as Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Convolutional Neural Networks (CNN), and Transformer-based architectures. The paper further analyzes publicly available benchmark datasets, feature engineering techniques, evaluation metrics, and practical applications in 5G, beyond-5G, and emerging 6G communication systems. A comparative assessment highlights the strengths, limitations, computational complexity, and prediction accuracy of various models across diverse networking scenarios. Furthermore, the study identifies key research challenges, including scalability, real-time processing, heterogeneous IoT environments, data privacy, model interpretability, and energy-efficient deployment at the network edge. Finally, future research directions are discussed, emphasizing federated learning, edge intelligence, explainable artificial intelligence (XAI), graph neural networks, digital twin-enabled networking, and reinforcement learning for autonomous traffic management. This review provides researchers and practitioners with a comprehensive understanding of the current state of machine learning-driven traffic prediction and offers valuable insights into the development of intelligent, adaptive, and sustainable telecommunication and IoT networks.