STAR: Semantic-Criticality Tiered Allocation of Radio Resources via Zero-Shot Learning

arXiv:2611.13238v1 Announce Type: cross Abstract: In remote inference, edge devices transmit high-resolution images over wireless links to a receiver, where an artificial intelligence (AI) model answers task-specific questions about these images to enable reliable decisions. These answers are often determined by a small set of task-causal pixels, such as those of text, whose corruption changes the answer, so these pixels must be delivered with high fidelity. Conventional transmission protects every pixel equally, so it either consumes many resource elements (REs) or corrupts these regions at l

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