English(EN) Adaptive Semantic Communication for Wireless Image Transmission Leveraging Mixture-of-Experts Mechanism

自适应MoE Swin Transformer增强无线图像传输

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-07 04:00

研究人员开发了一种新颖的自适应语义通信系统，用于无线图像传输，该系统在Swin Transformer架构中利用了混合专家（MoE）机制。该系统通过同时考虑图像内容和实时信道条件，将图像数据动态路由到专门的专家，克服了先前单一驱动路由方法的局限性。仿真结果表明，与现有方法相比，图像重建质量和传输效率得到了显著提高。 AI

影响这种自适应方法可以提高AI驱动的无线图像传输系统的鲁棒性和效率。

排序理由详细介绍新技术方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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Signal score: 7 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 详细介绍新技术方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, infra; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
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Story freshness: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Haowen Wan, Qianqian Yang · 2026-10-07 04:00

利用混合专家机制的自适应语义通信用于无线图像传输

arXiv:2604.02691v2 Announce Type: replace Abstract: Deep learning based semantic communication has achieved significant progress in wireless image transmission, but most existing schemes rely on fixed models and thus lack robustness to diverse image contents and dynamic channel c…

本文如何被排名

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利用混合专家机制的自适应语义通信用于无线图像传输

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