arXiv cs.AI· Albert Shaju, Christo Kurisummoottil Thomas, Mingzhe Chen·· 10 小时前AI 评分24
STAR：基于零样本学习的语义关键性分级无线资源分配框架
STAR: Semantic-Criticality Tiered Allocation of Radio Resources via Zero-Shot Learning
AI 导读
研究者提出零样本、领域无关的STAR框架，从冻结视觉语言模型的注意力中导出逐像素语义关键性指标（SCI），无需重训练即可通过更换提示词适配新任务与领域。在5G New Radio链路仿真中，STAR在$E_s/N_0 \geq -4$ dB时语义准确率至少达0.98，并在工厂与车辆监控数据集上分别将鲁棒编码传输的RE减少59.5%和91.5%，后者未改动任何模型。
正文 · 原文
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 low signal-to-noise ratio (SNR). To overcome these limitations, in this paper, a zero-shot, domain-agnostic framework, termed semantic-criticality tiered allocation of resources (STAR), is proposed. In particular, a per-pixel semantic-criticality indicator (SCI) is derived from the attention of a frozen vision-language model, so new tasks and domains are handled by changing only the prompt, without retraining. The resulting task causality-aware resource allocation problem over protection tiers is solved exactly by a threshold rule on the SCI, realized by a coded tier for the task-causal pixels and an uncoded, downsampled base layer for the full image. Simulations over a 5G New Radio link demonstrate that STAR attains a semantic accuracy of at least 0.98 for $E_s/N_0 \geq -4$ dB and, at comparable semantic reliability, reduces the REs of robust coded transmission by 59.5% and 91.5% on factory- and vehicle-monitoring datasets, respectively, the latter without any change to the models. Although a deep joint source-channel coding baseline uses fewer REs at comparable semantic accuracy, it must be retrained with labeled data for every new task or domain.
| Comments: | 6 pages, 9 figures, 1 table. Submitted to IEEE ICC 2027 |
| Subjects: | Signal Processing (eess.SP); Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV) |
| Cite as: | arXiv:2611.13238 [eess.SP] |
| (or arXiv:2611.13238v1 [eess.SP] for this version) | |
| https://doi.org/10.48550/arXiv.2611.13238 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Albert Shaju [view email]
[v1]
Mon, 9 Nov 2026 21:07:26 UTC (10,560 KB)
来源：arXiv cs.AI · arxiv.org