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New ML framework AUGUSTE slashes 5G URLLC scheduling latency

By PulseAugur Editorial · [2 sources] ·

Researchers have developed AUGUSTE, a novel machine learning framework designed to improve the efficiency of 5G Ultra Reliable Low Latency Communications (URLLC) scheduling. This system embeds online ML models within the Medium Access Control scheduler to predict packet arrivals and proactively allocate resources, thereby reducing latency. AUGUSTE achieves median round-trip times of approximately 10 ms, comparable to always-on scheduling, while using significantly less resource overhead. AI

IMPACT This framework could significantly improve real-time applications like industrial automation and autonomous systems by reducing network latency.

RANK_REASON The cluster contains an academic paper detailing a new ML-based scheduling framework for 5G networks.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New ML framework AUGUSTE slashes 5G URLLC scheduling latency

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The cluster contains an academic paper detailing a new ML-based scheduling framework for 5G networks.
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COVERAGE [2]

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

    AUGUSTE: Online-Learning dApp for Predictive URLLC Scheduling

    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: Online-Learning dApp for Predictive URLLC Scheduling

    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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