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English(EN) AUGUSTE: Online-Learning dApp for Predictive URLLC Scheduling

新的机器学习框架 AUGUSTE 削减 5G URLLC 调度延迟

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了 AUGUSTE，一个新颖的机器学习框架，旨在提高 5G 超可靠低延迟通信 (URLLC) 调度的效率。该系统将在线机器学习模型嵌入到媒体访问控制调度器中，以预测数据包到达并主动分配资源，从而降低延迟。AUGUSTE 实现的平均往返时间约为 10 毫秒，与始终在线调度相当，同时使用的资源开销显著减少。 AI

影响 该框架通过降低网络延迟，有可能显著改善工业自动化和自主系统等实时应用。

排序理由 该集群包含一篇详细介绍用于 5G 网络的新型基于机器学习的调度框架的学术论文。

在 arXiv cs.AI 阅读 →

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新的机器学习框架 AUGUSTE 削减 5G URLLC 调度延迟

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该集群包含一篇详细介绍用于 5G 网络的新型基于机器学习的调度框架的学术论文。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Maxime Elkael, Michele Polese, Yunseong Lee, Koichiro Furueda, Tommaso Melodia ·

    AUGUSTE: 用于预测性URLLC调度的在线学习dApp

    arXiv:2606.03664v1 Announce Type: cross Abstract: Ultra Reliable and Low Latency Communications (URLLC) was one of the main motivations behind 5G, with 3GPP advertising 1-10 ms latency targets for applications such as industrial automation, Vehicle-To-Everything (V2X), tactical e…

  2. arXiv cs.AI TIER_1 English(EN) · Tommaso Melodia ·

    AUGUSTE: 用于预测性URLLC调度的在线学习dApp

    Ultra Reliable and Low Latency Communications (URLLC) was one of the main motivations behind 5G, with 3GPP advertising 1-10 ms latency targets for applications such as industrial automation, Vehicle-To-Everything (V2X), tactical edge networking, and unmanned-system control. Years…

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