  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.
