It is critical to dimension (accurately estimate capacity of) a satellite system prior to deployment, as it is very expensive to reconfigure launched satellite systems that fail to meet demand or that waste capacity. The fundamental requirement is a dimensioning rule for resource blocks (RBs) given a satellite footprint and a target overload probability (target Quality-of-Service). The rule must be robust to the spatial covariance structure of signal attenuation, which is generally unknown both at the time of pre-deployment dimensioning and afterwards. Existing approaches address parts of this problem, but there does not yet exist a footprint-level RB dimensioning rule for the satellite context. We develop such a rule: starting with a Gaussian attenuation field that induces a covariance structure inspired by classical work on spatial covariance of attenuation, we sample users at random along with their field-based attenuation values, and estimate aggregate RB demand for a target overload probability. We do this in two complementary ways: a Monte Carlo route that gives a simulation-derived RB budget for a given target overload probability, and a concentration route that gives a conservative analytic upper bound on the target overload probability for a given RB budget (such as the one obtained through simulation). Taken together, these complementary approaches give a principled way to dimension RBs for a satellite footprint under spatially correlated attenuation.

翻译：在部署卫星系统前进行容量估算（精确评估其容量）至关重要，因为重新配置已发射升空但无法满足需求或容量过剩的卫星系统成本极高。核心需求是针对卫星覆盖区域和目标过载概率（服务质量指标）制定资源块（RB）的配置规则。该规则必须对信号衰减的空间协方差结构具有鲁棒性，而该结构在部署前后通常均不可知。现有方法仅能解决部分问题，目前尚无适用于卫星场景的覆盖级RB配置规则。我们提出如下规则：首先构建受经典衰减空间协方差研究启发的协方差结构高斯衰减场，随机采样用户及其对应的场衰减值，再针对目标过载概率估算总RB需求量。我们通过两种互补方法实现：蒙特卡洛方法可针对给定目标过载概率导出基于仿真的RB预算，而浓度方法可针对给定RB预算（如仿真所得）导出目标过载概率的保守解析上界。二者结合，为空间相关衰减条件下卫星覆盖区域的RB配置提供了原理性方法。