A probabilistic alternative to coverage analysis in uniform random wireless networks

The analysis of coverage probability based on signal-to-interference-plus-noise-ratio (SINR) is a classical problem in the study of wireless and cellular networks. Stochastic geometry (SG) has opened up the possibility of accurate coverage characterization for random spatial deployment of base stations. While results obtained using SG are tractable and compact, in most cases, they are usually in the form of incomplete integrals, which need to be efficiently computed. Although that is possible with the computational capabilities available today, it masks the underlying structure in the analysis precluding the possibility of using it for solving optimization and system design problems. This paper provides an alternate approach to analyzing SINR-based coverage probability using direct probability computation. We analyze a uniformly random wireless network as a test case and compare the analytical results with widely accepted frameworks in the SG literature. Our analytical derivations, validated through simulation studies, agree with well-known results in the literature. The developed approach provides the groundwork for coverage analysis in more complex network scenarios and channel conditions.