Capacity Scalability of LEO Constellations With Dynamic Link Failures

TL;DR

This paper analyzes how the capacity of LEO satellite constellations scales with size under dynamic link failures, revealing an upper bound that decreases with the number of satellites and depends on protocol and maintenance parameters.

Contribution

It establishes the fundamental relationship between constellation size, protocol overhead, and capacity scalability, providing bounds and optimal deployment insights.

Findings

Capacity scalability upper bound is O(1/n) with respect to constellation size.

Perfect topology information allows achieving the upper bound via shortest-path routing.

An optimal constellation size exists for maximum capacity scalability, beyond which scalability diminishes.

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.