Performance Analysis of Mega-Constellation Networks Based on a Two-State Markov Process

Low Earth orbit mega-constellations demonstrate unprecedented growth in global connectivity infrastructure, but their rapid orbital velocity causes frequent channel variations and persistent co-channel interference, particularly in densely populated orbital shells. Therefore, we introduce an innovative analytical framework that integrates stochastic geometry with Markov channel modeling to address these challenges. In our approach, the spatial distributions of satellites and terrestrial users are modeled using homogeneous Poisson point processes, and elevation-dependent channel variations are represented by a two-state Markov model. We provide a closed-form solution for coverage probability under correlated interference environments, jointly considering large-scale and small-scale fading characteristics. Verification through Monte Carlo simulations demonstrates precise alignment between theoretical predictions and empirical outage probability measurements.