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

[2508.15307] Co-optimizing Topology and Geometry in Mega-Constellation Networks via Structural Motif-Lattice Paradigm Skip to main content Search Submit Donate Log in Computer Science > Networking and Internet Architecture arXiv:2508.15307 (cs) [Submitted on 21 Aug 2025 ( v1 ), last revised 27 Aug 2026 (this version, v3)] Title: Co-optimizing Topology and Geometry in Mega-Constellation Networks via Structural Motif-Lattice Paradigm Authors: Xiangtong Wang View PDF HTML (experimental) 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. Subjects: Networking and Internet Architecture (cs.NI) Cite as: arXiv:2508.15307 [cs.NI] (or arXiv:2508.15307v3 [cs.NI] for this version) https://doi.org/10.48550/arXiv.2508.15307 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Xiangtong Wang [ view email ] [v1] Thu, 21 Aug 2025 07:00:52 UTC (3,236 KB) [v2] Tue, 25 Nov 2025 15:54:28 UTC (10,453 KB) [v3] Thu, 27 Aug 2026 09:17:41 UTC (12,348 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.NI < prev | next > new | recent | 2025-08 Change to browse by: cs References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from (opens in new tab) (opens in new tab) (opens in new tab)

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