Resilience Characterization of AI-Native Wireless Receivers via Persistent Homology
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. TRI quantifies the structural stability of a neural network receiver's parameter space during online adaptation to non-stationary channels. Specifically, TRI captures resilience through three complementary dimensions: (i) validation-loss resilience measuring model-channel...
| Authors | Christo Kurisummoottil Thomas, Emilio Calvanese Strinati |
|---|---|
| Primary category | cs.IT |
| Published | 21 May 2026 |
| Last updated at source | 21 May 2026 |
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