While stochastic geometry provides a powerful framework for the analysis of cellular networks, standard Monte Carlo simulations often suffer from slow convergence due to the stochasticity of the infinite far-field. This work introduces the \textit{Rao-Blackwellized Hybrid Estimator} (RBHE), which enhances simulation efficiency by analytically marginalizing the residual far-field interference via the conditional Laplace functional. By partitioning the interference field into $K$ dominant interferers and an infinite tail, we derive an estimator that combines exact spatial sampling with a rigorous analytical representation. We prove that the RBHE is an unbiased estimator for any finite truncation, while its systematic bias relative to the infinite-plane benchmark decays at a rate of $\mathcal{O}(K^{1-η/2})$. Numerical results demonstrate significant sample parsimony; in the high-reliability regime ($T = -10$ dB) with $K=2$, the RBHE yields a variance reduction gain of $90.75\times$, enabling a $98.90\%$ reduction in the spatial realizations required to reach a target precision. This framework effectively bridges the gap between tractable analytical models and high-fidelity simulations.

翻译：尽管随机几何为蜂窝网络分析提供了强大的框架，但由于无限远场的随机性，标准蒙特卡洛模拟通常收敛缓慢。本文提出了一种**拉奥-布莱克韦尔化混合估计器**（RBHE），它通过利用条件拉普拉斯泛函对残余远场干扰进行解析边缘化，从而提升了模拟效率。通过将干扰场划分为$K$个主导干扰源和无限尾部，我们推导出一种将精确空间采样与严格解析表示相结合的估计器。我们证明了对于任意有限截断，RBHE是无偏估计器，同时其相对于无限平面基准的系统性偏差以$\mathcal{O}(K^{1-η/2})$的速率衰减。数值结果表明了显著的样本节约性；在$K=2$的高可靠性区域（$T = -10$ dB）中，RBHE实现了$90.75$倍的方差缩减增益，使达到目标精度所需的空间实现数量减少了$98.90\%$。该框架有效地弥合了易处理解析模型与高保真模拟之间的差距。