Stabilizing and Optimizing Inter-Shell Routing in LEO Networks with Integrated Routing Cost

TL;DR

This paper introduces the DP-IRC algorithm for LEO satellite networks, optimizing inter-shell routing by balancing hop counts and link stability, significantly reducing switching rates while maintaining efficient paths.

Contribution

The paper presents a novel dynamic programming-based routing algorithm that explicitly incorporates switching costs, improving stability and efficiency in multi-shell LEO satellite networks.

Findings

DP-IRC reduces inter-shell ISL switching rates by over 39%.

The algorithm maintains near-optimal end-to-end path distances.

Experimental results validate improved stability over existing strategies.

Abstract

The low Earth orbit (LEO) mega-constellation network (LMCN), which uses thousands of satellites across multi-shell architectures to deliver different services, is facing challenges in inter-shell routing stability due to dynamic network topologies and frequent inter-satellite link (ISL) switching. Existing strategies, such as the Minimum Hop Path set, prioritize minimizing hop counts to reduce latency, but ignore ISL switching costs, which leads to high instability. To overcome this, the Adaptive Path Routing Scheme introduces path similarity thresholds to reduce the ISL switching frequency between shells. However, the greedy approach of Adaptive Path Routing Scheme is often trapped in local optima, sacrificing inter-shell path distance efficiency. To address these limitations, we propose the Dynamic Programming-based Integrated Routing Cost (DP-IRC) algorithm, which is designed explicitly for inter-shell routing optimization. By formulating multi-shell paths as a multistage decision problem, DP-IRC balances hop counts and ISL stability through an Integrated Routing Cost (IRC) metric, combining inter-/intra-shell hops and switching costs. Experiments over 60 time slots with real-world Starlink and OneWeb configurations show that DP-IRC reduces inter-shell ISL switching rates by 39.1% and 22.0% compared to the Minimum Hop Path set strategy and Adaptive Path Routing Scheme, respectively, while still maintaining near-optimal end-to-end distances.