Unlocking the Performance Potential of Mega-Constellation Networks: An Exploration of Structure-Building Paradigms

Abstract

Mega-constellation networks (MCNs) are transforming global internet access by providing ubiquitous connectivity to millions of users worldwide. The design of MCNs is crucial for achieving high-performance space-based internet, yet presents a significant challenge due to the large scale and tightly coupled parameters of these systems, which result in a high-dimensional combinatorial optimization problem. To address this challenge, we propose the Structured Motif Lattice (SML) paradigm, which decomposes the MCN design space into two orthogonal dimensions: topological connectivity and geometric layout. This decomposition reduces the original high-dimensional problem to a tractable bi-dimensional. Under the SML paradigm, we formalize the High-Availability and Low-Latency MCN Design (HALLMD) problem and develop the Lattice and Motif Search (LAMS) algorithm to find near-optimal MCN configurations. Experimental results demonstrate that the LAMS under the SML paradigm achieves substantially higher network availability and lower average traffic latency than the structures generated by current state-of-the-art methods, confirming the effectiveness of our approach.