Capacity Scalability of LEO Constellations With Dynamic Link Failures

2026-05-12

AI Summary

This study addresses the impact of dynamic link failures in low Earth orbit (LEO) satellite constellations, which disrupt network connectivity and symmetry, thereby increasing protocol overhead and reducing effective capacity; however, the relationship between capacity and constellation scale remains unclear. The work formally introduces “capacity scalability” as the ratio of capacity to protocol overhead under fault-free conditions and models link dynamics using a two-state Markov chain. Under uniform traffic and shortest-path routing, an upper bound on capacity scalability is derived. Theoretical analysis reveals that this metric decays as $O(1/n)$ with constellation size $n$, implying an optimal scale that maximizes capacity scalability. While extending the maintenance cycle improves performance, it cannot prevent the metric from asymptotically approaching zero in extremely large constellations.

Abstract

Dynamic link failures disrupt the connectivity and geometric symmetry of the constellation structure, thereby increasing protocol overhead and degrading the effective capacity for traffic transport. The fundamental relationship between constellation size and effective capacity under protocol overhead constraints remains unclear. To this end, we define capacity scalability as the ratio of constellation capacity under non-failure conditions to protocol overhead. Specifically, if ISL states follow a two-state discrete Markov chain and the maintenance period is $k \geq 1$, the upper bound of capacity scalability under the uniform traffic pattern is $O(1/n)$, where $n$ is the number of satellites. With perfect information about the constellation topology, the upper bound can be achieved via shortest-path routing. For any given protocol, there exists an optimal constellation deployment scale in terms of capacity scalability. When the constellation size is below this optimum scale, capacity scalability increases with constellation size, thereby improving effective capacity. Increasing the maintenance period $k$ can improve capacity scalability, but it does not change the fact that the capacity scalability converges to zero when the constellation size exceeds the optimal scale.

Problem

• LEO constellations
• dynamic link failures
• capacity scalability
• protocol overhead
• effective capacity

Innovation

• capacity scalability
• LEO constellations
• dynamic link failures
• protocol overhead
• Markov chain modeling