Sample entropy for graph signals: An approach to nonlinear dynamic analysis of data on networks
2026-04-08T08:51:40Z•6406d1791948bb072b4c213e14ad143efdb598f0e1d6f0a5c1fab094c54a200d
ADMMDDA‑NetDoppler‑delay‑angleIMREDLLM knowledge baseSampEnGU‑Netchannel estimationcross‑domain fusion codec (CDFC)deep learningdeep unfoldingdefect detectiondiffusion modelsenergy concentration indexgraph neural networksgraph signalshallucination filteringmassive MIMOmicro‑Dopplerprimal‑dualradarrespiration ratesample entropysemantic communicationwireless resource allocation
What happened
A batch of recent signal‑processing and wireless communications preprints covering: SampEnG (a topology‑aware generalization of Sample Entropy for graph signals); graph‑signal diffusion models (U‑Net/GNN diffusion policy) for constrained wireless resource allocation; a deep‑learning quasi‑stationary slice detector for radar respiration under large body motion; DDA‑Net, a 3D deep‑unfolding ADMM architecture for TDD massive‑MIMO channel estimation in Doppler‑Delay‑Angle domain; an operator‑theoretic Energy Concentration Index and IMRED detector for impulse‑excited defect detection; semantic‑awar
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_eess_sp
- Record identifier
- 6406d1791948bb072b4c213e14ad143efdb598f0e1d6f0a5c1fab094c54a200d
- Enrichment time
- 2026-04-08T08:51:40Z
- AI-assisted enrichment
- Yes
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