Digital Twins (DTs) are set to become a key enabling technology in future wireless networks, with their use in network management increasing significantly. We developed a DT framework that leverages the heterogeneity of network access technologies as a resource for enhanced network performance and management, enabling smart data handling in the physical network. Tested in a \textit{Campus Area Network} environment, our framework integrates diverse data sources to provide real-time, holistic insights into network performance and environmental sensing. We also envision that traditional analytics will evolve to rely on emerging AI models, such as Generative AI (GenAI), while leveraging current analytics capabilities. This capacity can simplify analytics processes through advanced ML models, enabling descriptive, diagnostic, predictive, and prescriptive analytics in a unified fashion. Finally, we present specific research opportunities concerning interoperability aspects and envision aligning advancements in DT technology with evolved AI integration.

翻译：数字孪生（DTs）有望成为未来无线网络的一项关键使能技术，其在网络管理中的应用将显著增加。我们开发了一个数字孪生框架，该框架将网络接入技术的异构性作为一种资源加以利用，以提升网络性能和管理能力，从而在物理网络中实现智能数据处理。在\textit{园区网络}环境中测试表明，我们的框架集成了多种数据源，能够提供关于网络性能和环境感知的实时、整体性洞察。我们还预见，传统分析将演进为依赖生成式人工智能（GenAI）等新兴AI模型，同时利用现有的分析能力。这种能力可以通过先进的机器学习模型简化分析流程，以统一的方式实现描述性、诊断性、预测性和规范性分析。最后，我们提出了关于互操作性方面的具体研究机遇，并展望了将数字孪生技术的进展与不断演进的AI集成相结合的前景。