Proposes Topological Resilience Index (TRI) via persistent homology to quantify resilience of deep learning OFDM receivers to channel shifts, claiming superior warning lead and BER reduction in simulations across ITU-R transitions.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Defines finite recognition lengths for knots using ropelength-filtered lifted Reidemeister graphs and characteristic patterns derived from the Barbensi-Celoria reconstruction theorem.
citing papers explorer
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Resilience Characterization of AI-Native Wireless Receivers via Persistent Homology
Proposes Topological Resilience Index (TRI) via persistent homology to quantify resilience of deep learning OFDM receivers to channel shifts, claiming superior warning lead and BER reduction in simulations across ITU-R transitions.
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Finite Knot Theory via Ropelength-Filtered Reidemeister Graphs
Defines finite recognition lengths for knots using ropelength-filtered lifted Reidemeister graphs and characteristic patterns derived from the Barbensi-Celoria reconstruction theorem.