Technology Category
Search and Optimization: Evaluation and AnalysisPlanning, Routing, and Scheduling: Scheduling under UncertaintyMachine Learning: Evaluation and Analysis
Optimal Handover Strategies in LEO Satellite Networks
🤖 AI Summary
Existing LEO satellite constellation capacity analyses often rely on restrictive assumptions—such as short service durations—that hinder practical handover strategy design. This paper proposes a general ergodic capacity analysis framework: first, a persistent satellite channel model is established; second, renewal process theory is leveraged to formulate a capacity characterization that uncovers the intrinsic relationship between persistent and non-persistent capacity; third, shadowed Rician fading is incorporated, and a variant of Dinkelbach’s algorithm is employed to solve the resulting nonlinear fractional programming problem, complemented by closed-form upper and lower bounds to reduce computational complexity. The key contribution is an explicit, analytically derived optimal handover decision rule, which proves that simple, threshold-based handover policies can closely approach theoretical optimality—achieving both mathematical rigor and engineering deployability.
Technology Category
Application Category
📝 Abstract
Existing theoretical analyses of satellite mega-constellations often rely on restrictive assumptions, such as short serving times, or lack tractability when evaluating realistic handover strategies. Motivated by these limitations, this paper develops a general analytical framework for accurately characterising the ergodic capacity of low Earth orbit (LEO) satellite networks under arbitrary handover strategies. Specifically, we model the transmission link as shadowed-Rician fading and introduce the persistent satellite channel, wherein the channel process is governed by an i.i.d. renewal process under mild assumptions of uncoordinated handover decisions and knowledge of satellite ephemeris and fading parameters. Within this framework, we derive the ergodic capacity (persistent capacity) of the persistent satellite channel using renewal theory and establish its relation to the non-persistent capacity studied in prior work. To address computational challenges, we present closed-form upper and lower bounds on persistent capacity. The optimal handover problem is formulated as a non-linear fractional program, obtaining an explicit decision rule via a variant of Dinkelbach's algorithm. We further demonstrate that a simpler handover strategy maximising serving capacity closely approximates the optimal strategy, providing practical insights for designing high-throughput LEO satellite communication systems.
Problem
Research questions and friction points this paper is trying to address.
Develops analytical framework for LEO satellite ergodic capacity
Derives bounds on persistent capacity and optimal handover strategies
Proposes practical handover strategy for high-throughput satellite systems
Innovation
Methods, ideas, or system contributions that make the work stand out.
General analytical framework for LEO satellite ergodic capacity
Closed-form bounds on persistent capacity using renewal theory
Optimal handover via Dinkelbach's algorithm and simplified strategy
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