AI RESEARCH
Resilience Characterization of AI-Native Wireless Receivers via Persistent Homology
arXiv CS.LG
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ArXi:2605.22886v1 Announce Type: cross AI-native wireless receivers based on deep learning exhibit remarkable performance under stationary channel conditions, yet their resilience to distributional shifts remains poorly characterized by conventional metrics such as bit error rate (BER). To overcome these limitations, this paper proposes a novel real-time metric, the Topological Resilience Index (TRI), grounded in persistent homology and persistence exponents.