Algebraic and Geometric Models for Space Networking
April 03, 2023 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
William Bernardoni, Robert Cardona, Jacob Cleveland, Justin Curry, Robert Green, Brian Heller, Alan Hylton, Tung Lam, Robert Kassouf-Short
arXiv ID
2304.01150
Category
math.AT
Cross-listed
cs.LG,
cs.NI,
math.CT
Citations
3
Venue
arXiv.org
Last Checked
1 month ago
Abstract
In this paper we introduce some new algebraic and geometric perspectives on networked space communications. Our main contribution is a novel definition of a time-varying graph (TVG), defined in terms of a matrix with values in subsets of the real line P(R). We leverage semi-ring properties of P(R) to model multi-hop communication in a TVG using matrix multiplication and a truncated Kleene star. This leads to novel statistics on the communication capacity of TVGs called lifetime curves, which we generate for large samples of randomly chosen STARLINK satellites, whose connectivity is modeled over day-long simulations. Determining when a large subsample of STARLINK is temporally strongly connected is further analyzed using novel metrics introduced here that are inspired by topological data analysis (TDA). To better model networking scenarios between the Earth and Mars, we introduce various semi-rings capable of modeling propagation delay as well as protocols common to Delay Tolerant Networking (DTN), such as store-and-forward. Finally, we illustrate the applicability of zigzag persistence for featurizing different space networks and demonstrate the efficacy of K-Nearest Neighbors (KNN) classification for distinguishing Earth-Mars and Earth-Moon satellite systems using time-varying topology alone.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β math.AT
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Persistence Diagrams with Linear Machine Learning Models
R.I.P.
π»
Ghosted
Comparing persistence diagrams through complex vectors
R.I.P.
π»
Ghosted
A Riemannian Framework for Statistical Analysis of Topological Persistence Diagrams
R.I.P.
π»
Ghosted
Path homologies of deep feedforward networks
R.I.P.
π»
Ghosted
From trees to barcodes and back again: theoretical and statistical perspectives
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Language Models are Few-Shot Learners
R.I.P.
π»
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
π»
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
π»
Ghosted