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The emergence of new wireless technologies and use cases is increasing the complexity of network configuration and performance optimization. Relying on a virtual twin to test and analyze network and equipment performance is an efficient solution for evaluating a multitude of static and mobility scenarios as well as considerably reducing the effort needed for field-based validation. Precise digital representations of environments incorporating wireless network elements and accurate RF simulations represent key assets in understanding complex phenomena of wireless networks as well as generating large volumes of datasets that can support the training of PHY/MAC AI models and optimization algorithms. Exploring key concepts of its approach to support wireless network digital twin initiatives during the various phases of network development (equipment performance assessment, planning, operations) as well as detailing some real use cases where the technology is leveraged.