Co-optimizing Topology and Geometry in Mega-Constellation Networks via Structural Motif-Lattice Paradigm

Abstract

Mega-constellation networks (MCNs) are currently revolutionizing global internet accessibility by providing ubiquitous connectivity on a planetary scale. While the architectural configuration of MCNs is paramount to achieving high-performance space-based networking, the design process is inherently complex. This complexity stems from the vast system scale and tightly coupled parameters, which culminate in a high-dimensional combinatorial optimization challenge. To address this challenge, we propose the Structural Motif-Lattice (SML) paradigm, a framework that decouples the MCN design space into two independent dimensions: topological connectivity (defining inter-satellite link logic) and geometric layout (defining the spatial distribution of satellites). This decomposition is theoretically justified by the inherent separability of connectivity logic and spatial distribution in MCN architecture, thereby reducing the original high-dimensional problem to a tractable bi-dimensional optimization task. Within the SML paradigm, we formalize the Reliable and Low-latency MCN Design problem and develop the Progressive Motif and Lattice Search (PLAMS) algorithm to find near-optimal MCN configurations. Experiments conducted on major constellations including Starlink, OneWeb, Kuiper, and Telesat demonstrate that PLAMS achieves performance comparable to or better than state-of-the-art methods, yielding substantially enhanced network reliability and significant reductions in average propagation latency.