Ergodic Capacity and Optimal Handover in Satellite Mega-Constellations under Finite Serving Times

TL;DR

This paper presents a new framework for analyzing ergodic capacity in LEO satellite mega-constellations under realistic handover strategies and finite serving times, using stochastic modeling and optimization techniques.

Contribution

It introduces a comprehensive model for satellite link capacity considering arbitrary handover strategies, and derives explicit optimal handover decision rules.

Findings

Optimal handover strategies significantly improve capacity.

Simpler maximization strategies perform nearly as well as complex ones.

Closed-form bounds enable efficient capacity evaluation.

Abstract

Existing analyses of ergodic capacity in satellite mega-constellations often rely on restrictive serving time assumptions or become intractable under realistic handover strategies. This paper develops a framework for characterising the ergodic capacity of low-Earth-orbit (LEO) mega-constellation links under arbitrary handover strategies and serving times. The user--satellite link is modelled as shadowed-Rician fading, and a semi-stochastic satellite channel with persistence is introduced in which visible satellites are drawn from a non-homogeneous binomial point process (NBPP) at each handover and the selected satellite is then propagated using circular orbit dynamics. Under uncoordinated handover decisions, this yields independent serving periods and enables a renewal-theoretic derivation of persistent capacity. This capacity is related to the non-persistent capacity from prior work, and closed-form bounds are provided for efficient evaluation. Optimal handover is then formulated as a non-linear fractional program, yielding an explicit decision rule via a variant of Dinkelbach's algorithm. The results show that a simpler strategy that maximises serving capacity closely approximates the optimum while performing best under SGP4-based orbit prediction and mega-constellation simulation.