A Hybrid Dominant-Interferer Approximation for SINR Coverage in Poisson Cellular Networks

2025-11-24

AI Summary

Accurate and computationally tractable SINR coverage analysis in Poisson cellular networks remains challenging due to inherent trade-offs between precision and analytical feasibility. Method: This paper proposes a hybrid approximation framework: Monte Carlo sampling for dominant near-field interferers, and Laplace functional modeling for the residual far-field interference. Contribution/Results: The approach eliminates reliance on nested integrals and special functions in classical stochastic geometry models, while avoiding failure modes of probabilistic interference models under missing interference moments or restrictive parameter assumptions. Its modular design ensures numerical stability and path-loss independence, and—uniquely—provides a theoretically derived error bound that converges as the number of dominant interferers increases. Experiments demonstrate high accuracy and low computational overhead under both noise-limited and interference-limited regimes, with strong robustness and consistency across diverse channel conditions and network deployment parameters.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

Accurate radio propagation and interference modeling is essential for the design and analysis of modern cellular networks. Stochastic geometry offers a rigorous framework by treating base station locations as a Poisson point process and enabling coverage characterization through spatial averaging, but its expressions often involve nested integrals and special functions that limit general applicability. Probabilistic interference models seek closed-form characterizations through moment-based approximations, yet these expressions remain tractable only for restricted parameter choices and become unwieldy when interference moments lack closed-form representations. This work introduces a hybrid approximation framework that addresses these challenges by combining Monte Carlo sampling of a small set of dominant interferers with a Laplace functional representation of the residual far-field interference. The resulting dominant-plus-tail structure provides a modular, numerically stable, and path-loss-agnostic estimator suitable for both noise-limited and interference-limited regimes. We further derive theoretical error bounds that decrease with the number of dominant interferers and validate the approach against established stochastic geometry and probabilistic modeling benchmarks.

Problem

• Modeling SINR coverage in Poisson cellular networks accurately
• Addressing computational complexity in stochastic geometry interference models
• Providing tractable approximations for interference moments without closed forms

Innovation

• Hybrid Monte Carlo sampling for dominant interferers
• Laplace functional representation for residual interference
• Modular path-loss-agnostic estimator with error bounds