Resilience Characterization of AI-Native Wireless Receivers via Persistent Homology
2026-05-25T08:51:45Z•f123efff740209da124c0628d98ae63b3bf07ed2f0c0be80a892c790f02ea277
AI-native receiversAPN exponentsGrassmannian partitionsMDSNMDSOFDMReed–Muller codesReed–Solomonboomerang uniformitycharacteristic 3coding theorycryptographygraph isomorphisminformation theorylocal alignmentpermutation equivalencepersistent homologyquantum error correctionresilience metricssum-free functionssurface codes','MWPM','posterior MWPM','XYZ planar code' ,topological data analysistorn paper channeltwisted generalized Reed–Solomonwireless communications
What happened
This document is an arXiv RSS batch of new math/information-theory papers (May 25 2026) covering advances in coding theory, wireless/communication systems, learning in networks, and cryptographic function analysis. Highlights include: a Topological Resilience Index (TRI) using persistent homology to detect and guide online re‑adaptation of AI-native OFDM receivers under non‑stationary channels; new structural results connecting k‑th order sum‑free functions to Reed–Muller subcodes and Grassmannian partitions; a local‑alignment scheme for the torn‑paper channel that raises achievable rates and扱
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_math_it
- Record identifier
- f123efff740209da124c0628d98ae63b3bf07ed2f0c0be80a892c790f02ea277
- Enrichment time
- 2026-05-25T08:51:45Z
- AI-assisted enrichment
- Yes
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