  The growing complexity of next-generation networks exacerbates the modeling
and algorithmic flaws of conventional network optimization methodology. In this
paper, we propose a mobile network digital twin (MNDT) architecture for 6G
networks. To address the modeling and algorithmic shortcomings, the MNDT uses a
simulation-optimization structure. The feedback from the network simulation
engine, which serves as validation for the optimizer's decision outcomes, is
used explicitly to train artificial intelligence (AI) empowered optimizers
iteratively. In practice, we develop a network digital twin prototype system
leveraging data-driven technology to accurately model the behaviors of mobile
network elements (e.g., mobile users and base stations), wireless environments,
and network performance. An AI-powered network optimizer has been developed
based on the deployed MNDT prototype system for providing reliable and
optimized network configurations. The results of the experiments demonstrate
that the proposed MNDT infrastructure can provide practical network
optimization solutions while adapting to the more complex environment.
