Network digital twins: Tackling challenges and enhancing wireless network management

Abstract

In the age of rapid digital transformation, wireless networks are increasingly critical, connecting billions of devices and supporting bandwidth-heavy applications like virtual reality and HD video streaming. However, the surge in network traffic and the dynamic nature of modern networks have exposed limitations in traditional network management. These systems, which often rely on outdated data and lack predictive capabilities, struggle to manage the complexity of emerging technologies like 6G and the Internet of Things (IoT), leading to suboptimal performance, security vulnerabilities, and inefficiencies. This thesis explores Network Digital Twins (NDTs) as a solution to these challenges. NDTs are virtual replicas of physical networks that allow real-time monitoring, simulation, and optimization. By mirroring real network behavior, NDTs enable proactive management through predictive analytics and scenario simulations. These capabilities are vital for optimizing network performance, anticipating problems, and integrating new technologies seamlessly. The research introduces a new digital twin networking framework called T6CONF, designed for IPv6 infrastructures. This framework tackles communication and synchronization challenges in NDT ecosystems, ensuring robust, real-time network management. It incorporates a What-if Analysis module powered by AI, which generates synthetic data to simulate various network conditions and predict outcomes, improving decision-making across different network environments. Through various case studies, the thesis demonstrates how NDTs can enhance key performance metrics like throughput, latency, packet loss, and coverage. In WiFi networks, the proposed Digital Twin WiFi Network (DTWN) uses AI-based techniques to improve interference management and throughput. In wireless ad-hoc networks, NDTs optimize network selection and packet delivery, while in IoT networks, NDTs support context-aware data management, contributing to smart city initiatives and sustainability goals like net-zero carbon emissions. In conclusion, the thesis provides a comprehensive framework for implementing and evaluating NDTs in wireless network management. It highlights the potential of NDTs to improve network performance and scalability, paving the way for the future integration of emerging technologies. Through this research, NDTs are positioned as essential tools for managing the growing complexity of modern wireless networks.