This paper comprehensively investigates the integration of digital twins within 6G network infrastructures, emphasizing their transformative potential for network management and maintenance. By leveraging advanced simulations and theoretical analyses, we explore how digital twins can significantly enhance the capabilities of 6G networks, including improved efficiency, reduced latency, and predictive maintenance functionalities. Our research methodologies utilize a combination of MATLAB and NS3 simulations, which model complex network scenarios to evaluate the performance impacts of digital twins in real-time operations. Furthermore, our study delves into the unique challenges posed by the integration of such advanced technologies, including issues of data synchronization and security, proposing innovative solutions like edge computing for reduced latency and blockchain for enhanced security. The findings suggest that digital twins could play a crucial role in the deployment of 6G networks, offering substantial benefits over traditional network management approaches and paving the way for more reliable, efficient, and user-centric telecommunications infrastructures. This study not only bridges the current knowledge gap concerning digital twins in 6G but also lays the groundwork for future research in enhancing network operations through advanced digital replicas.

This paper presents a novel approach to managing resources efficiently in vehicular networks through the use of digital twins and generative artificial intelligence (AI). The increasing complexity and demands of vehicular communications necessitate dynamic and intelligent management systems. Our proposed framework employs digital twins to create real-time, virtual replicas of vehicular environments, enhancing the understanding of network states. Concurrently, generative AI models simulate potential network scenarios and predict outcomes under various conditions. This dual approach allows for preemptive adjustments to resource allocation, significantly improving network performance metrics such as throughput, latency, and reliability. The efficacy of this integrated system is demonstrated through rigorous testing in simulated urban environments, providing a scalable solution to the challenges of modern vehicular networks.