Sheaf-theoretic Signal Processing on Graphs: Spectral Theory, Filtering, and Sampling
Modern sensing, communication, and learning systems generate heterogeneous network signals, with local data differing in dimension, modality, and geometric structure. Processing such data requires a mathematical framework capable of simultaneously modeling heterogeneous local signal spaces and the transformations relating them. Network sheaves provide such a framework by associating local vector spaces with network entities and linear restriction maps with their interactions. This is the first paper to develop a unified sheaf signal processing (SSP) framework on network sheaves, extending the fundamental operations of signal processing, namely spectral analysis, filtering, and sampling, t...
| Authors | Gabriele D'Acunto, Leonardo Di Nino, Paolo Di Lorenzo et al. |
|---|---|
| Primary category | eess.SP |
| Published | 2 August 2026 |
| Last updated at source | 2 August 2026 |
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