Network planning seeks to determine base station parameters that maximize coverage and capacity in cellular networks. However, achieving optimal planning remains challenging due to the diversity of deployment scenarios and the significant simulation-to-reality discrepancy. In this paper, we propose \emph{AutoPlan}, a new automatic network planning framework by leveraging digital radio twin (DRT) techniques. We derive the DRT by finetuning the parameters of building materials to reduce the sim-to-real discrepancy based on crowdsource real-world user data. Leveraging the DRT, we design a Bayesian optimization based algorithm to optimize the deployment parameters of base stations efficiently. Using the field measurement from Husker-Net, we extensively evaluate \emph{AutoPlan} under various deployment scenarios, in terms of both coverage and capacity. The evaluation results show that \emph{AutoPlan} flexibly adapts to different scenarios and achieves performance comparable to exhaustive search, while requiring less than 2\% of its computation time.

翻译：网络规划旨在确定能够最大化蜂窝网络覆盖范围与容量的基站参数。然而，由于部署场景的多样性以及显著的仿真与现实差异，实现最优规划仍具挑战性。本文提出一种名为 \emph{AutoPlan} 的新型自动网络规划框架，该框架利用数字无线电孪生技术构建。我们通过基于众包真实世界用户数据微调建筑材料参数来减小仿真与现实差异，从而推导出数字无线电孪生模型。借助该孪生模型，我们设计了一种基于贝叶斯优化的算法，以高效优化基站的部署参数。利用 Husker-Net 的实地测量数据，我们在多种部署场景下对 \emph{AutoPlan} 的覆盖范围与容量性能进行了全面评估。评估结果表明，\emph{AutoPlan} 能够灵活适应不同场景，其性能可与穷举搜索相媲美，而所需计算时间不足后者的 2\%。