Given the necessity of connecting the unconnected, covering blind spots has emerged as a critical task in the next-generation wireless communication network. A direct solution involves obtaining a coverage manifold that visually showcases network coverage performance at each position. Our goal is to devise different methods that minimize the absolute error between the estimated coverage manifold and the actual coverage manifold (referred to as accuracy), while simultaneously maximizing the reduction in computational complexity (measured by computational latency). Simulation is a common method for acquiring coverage manifolds. Although accurate, it is computationally expensive, making it challenging to extend to large-scale networks. In this paper, we expedite traditional simulation methods by introducing a statistical model termed line-of-sight probability-based accelerated simulation. Stochastic geometry is suitable for evaluating the performance of large-scale networks, albeit in a coarse-grained manner. Therefore, we propose a second method wherein a model training approach is applied to the stochastic geometry framework to enhance accuracy and reduce complexity. Additionally, we propose a machine learning-based method that ensures both low complexity and high accuracy, albeit with a significant demand for the size and quality of the dataset. Furthermore, we describe the relationships between these three methods, compare their complexity and accuracy as performance verification, and discuss their application scenarios.

翻译：鉴于连接未连接设备的必要性，覆盖盲区已成为下一代无线通信网络中的关键任务。一个直接的解决方案是获得一个覆盖流形，直观地展示每个位置的网络覆盖性能。我们的目标是设计不同方法，使估计覆盖流形与实际覆盖流形之间的绝对误差最小化（即精度），同时最大化计算复杂度的降低（以计算延迟衡量）。仿真是一种获取覆盖流形的常用方法。尽管精度高，但仿真计算开销大，难以扩展到大规模网络。本文通过引入一种被称为基于视距概率加速仿真的统计模型，加快了传统仿真方法。随机几何适用于评估大规模网络的性能，尽管是粗粒度的方式。因此，我们提出第二种方法，在随机几何框架中应用模型训练方法以提高精度并降低复杂度。此外，我们提出一种基于机器学习的方法，确保低复杂度与高精度，尽管对数据集的大小和质量有较高要求。进一步，我们描述了这三种方法之间的关系，通过复杂度与精度的对比作为性能验证，并讨论了它们的应用场景。