Digital Twin-Based in Next-Generation Wi-Fi Networks: Survey and Future Challenges

The integration of Digital Twin (DT) technology into wireless communication networks, particularly in Wi-Fi systems, is emerging as a transformative approach to optimizing network resources and performance. As the demand for next-generation wireless services increases, particularly with the advancements in Wi-Fi 7 and the anticipated developments in Wi-Fi 8, addressing the complexities of resource allocation, interference management, and user behavior prediction becomes crucial. This paper explores the role of DTs in optimizing resource management for WiFi networks, focusing on design aspects, high-level architecture, and the challenges associated with their implementation. A detailed overview of current DT frameworks and technologies is provided, emphasizing their potential to improve network efficiency in dynamic and dense environments. Furthermore, key open research challenges are discussed, including the modeling of interfering access points, user mobility, and the application of data-driven machine learning approaches for proactive network management. By addressing these challenges, DTs can enable advanced use cases such as ultra-low latency communication, extended reality applications, and Internet of Everything (IoE) services. This paper offers valuable insights for future research in the development of DT-based solutions for resource optimization in Wi-Fi networks, with a focus on Wi-Fi 7 and Wi-Fi 8 as the next frontiers in wireless technology.