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.
Resilient radio access net- works: Ai and the unknown unknowns
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
A sequential Monte Carlo simulation approach with fixed-level splitting efficiently estimates rare non-recovery probabilities in resilient wireless networks and extends to generative models for digital twins.
The report assembles abstracts of invited talks, presentations, and posters from the FFCS conference on foundational limits and emerging paradigms in communication.
citing papers explorer
-
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.
-
Sequential Monte Carlo for Resilient Networks: Assessment, Mitigation, and Generative Modeling
A sequential Monte Carlo simulation approach with fixed-level splitting efficiently estimates rare non-recovery probabilities in resilient wireless networks and extends to generative models for digital twins.
-
Foundations of Future Communication Systems: Innovations in Communication - A Report
The report assembles abstracts of invited talks, presentations, and posters from the FFCS conference on foundational limits and emerging paradigms in communication.