农业物联网中基于贝叶斯优化的鲁棒基站部署
Robust Base Station Placement in Agricultural IoT via Bayesian Optimization
AI总结：
针对农业物联网中植被生长导致的信道变化，提出基于高斯过程贝叶斯优化的基站部署方法，最大化最差季节覆盖率，在50次射线追踪内实现72.8%最差覆盖率。
摘要：
基于私有5G NR的精准农业网络应确保整个作物生长季节中物联网传感器节点的可靠连接，然而随着植被生长和成熟，传播环境发生显著变化。我们将K个基站（BS）部署问题表述为一个最大化最差季节覆盖问题，该问题最大化所有作物生长阶段中最差的覆盖比例。由于每次目标评估需要对所有阶段进行昂贵的射线追踪模拟，我们采用高斯过程贝叶斯优化（GPBO）框架，该框架利用射线追踪构建鲁棒目标的概率代理模型。在一个1平方公里的多作物农场中，包含三个不同作物区域，在3.5 GHz频率下，所提出的方案在少于50次射线追踪评估中，使用K=3个基站实现了72.8%的最差覆盖率，在所有四个季节阶段中，性能优于预算匹配的最先进方法至少4.6个百分点。
英文摘要：
Precision-agriculture networks based on private 5G NR should ensure reliable connectivity for IoT sensor nodes throughout the crop growing season, yet the propagation environment changes dramatically as vegetation grows and matures. We formulate $K$-base-station~(BS) placement as a \textit{maximin seasonal coverage} problem that maximizes the worst-case coverage fraction across all crop growth stages. Since each objective evaluation requires expensive ray-tracing simulations across all stages, we adopt a Gaussian-process Bayesian optimization~(GPBO) framework that builds a probabilistic surrogate of the robust objective using ray tracing. On a $1\,\text{km}^2$ multi-crop farm with three distinct crop zones at $3.5\,\text{GHz}$, the proposed scheme achieves $72.8\%$ worst-case coverage with $K{=}3$ BSs in fewer than fifty ray-tracing evaluations, outperforming budget-matched state-of-the-art approaches by at least $4.6\,\text{pp}$ across all four seasonal stages.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
