Terrain-Based Coverage Manifold Estimation: Machine Learning, Stochastic Geometry, or Simulation?

Ruibo Wang, Washim Uddin Mondal, Mustafa A. Kishk, Vaneet Aggarwal, Mohamed Slim Alouini

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)1
Number of pages1
JournalIEEE Open Journal of the Communications Society
StateAccepted/In press - 2023


  • Analytical models
  • Atmospheric modeling
  • Buildings
  • Coverage manifold
  • machine learning
  • Manifolds
  • Materials requirements planning
  • simulation
  • stochastic geometry
  • Stochastic processes
  • terrain
  • Training

ASJC Scopus subject areas

  • Computer Networks and Communications


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