• AutoPlan automates cellular base‑station planning by integrating a digital radio twin (DRT) model. • DRT parameters are fine‑tuned using crowdsourced real‑world user data to shrink sim‑to‑real gaps. • A Bayesian optimization engine efficiently searches deployment settings, matching exhaustive search performance. • Field tests on Husker‑Net show <2% of exhaustive search computation time. • AutoPlan adapts flexibly to diverse deployment scenarios, optimizing coverage and capacity. • The framework reduces planning complexity while preserving near‑optimal network performance.

Article Summaries:

  • Automatic Network Planning with Digital Radio Twin

Researchers have introduced AutoPlan, an automated framework that uses a digital radio twin (DRT) to improve cellular network planning. The DRT is built by fine‑tuning building‑material parameters with crowdsourced real‑world data, reducing the simulation‑to‑real discrepancy. Leveraging this twin, a Bayesian‑optimization algorithm efficiently searches for optimal base‑station deployment settings. Evaluation on field measurements from the Husker‑Net dataset shows that AutoPlan matches the performance of exhaustive search while requiring less than 2 % of the computation time. The approach adapts flexibly to diverse deployment scenarios, offering a practical, low‑cost solution for network operators.

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