RESEARCHING ON SIMULATION APPROACHES IN MODELING INFOCOMMUNICATION NETWORKS

Abstract

This article investigates simulation approaches in the modeling of infocommunication networks, which have become increasingly complex due to the rapid development of 5G/6G technologies, IoT systems, and cloud-based infrastructures. The study analyzes modern simulation methodologies, including discrete-event simulation, hybrid modeling techniques, and AI-enhanced simulation frameworks. Particular attention is given to traffic modeling, network scalability, and performance evaluation under dynamic and heterogeneous conditions. The research highlights the integration of machine learning methods into simulation environments to improve prediction accuracy and adaptive network optimization. Additionally, challenges such as model validation, computational complexity, and interoperability are discussed. The results demonstrate that simulation-based approaches are essential for designing, analyzing, and optimizing next-generation communication networks, providing a flexible and efficient tool for researchers and engineers.