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UID:914@lincs.fr
DTSTART;TZID=Europe/Paris:20251210T140000
DTEND;TZID=Europe/Paris:20251210T150000
DTSTAMP:20260115T162852Z
URL:https://www.lincs.fr/events/llm-driven-scheduler-generation-for-intent
 -based-ran/
SUMMARY:LLM-Driven Scheduler Generation for Intent-Based RAN
DESCRIPTION:The shift toward open\, programmable O-RAN and AI-RAN 6G
 networks presents new opportunities for Intent-Based Networking (IBN) to
 dynamically optimize RAN operations. However\, applying IBN to the RAN
 scheduler—a key component for resource allocation—remains challenging.
 Existing methods rely on coarse network slicing\, lacking the granularity
 to adapt to individual user conditions and traffic. While many scheduling
 algorithms exist\, their use is limited by implementation complexity\, lack
 of systematic evaluation\, and deployment challenges. To overcome this\, we
 introduce ALLSTaR\, a novel framework that uses LLMs to automate
 intent-driven scheduler design\, implementation\, and evaluation. ALLSTaR
 translates natural language intents\, extracts scheduler logic from
 academic literature using OCR and LLMs\, and deploys them as O-RAN dApps on
 a production-grade\, multi-vendor 5G testbed. This enables the largest OTA
 comparison of 18 synthesized schedulers and forms the basis for an
 Intent-Based Scheduling system that dynamically selects and deploys the
 optimal scheduler. Our approach supports use cases beyond current slicing
 techniques\, enabling fine-grained control based on buffer status\, PHY
 conditions\, and diverse traffic types.\n\nPreprint 1:
 https://arxiv.org/pdf/2505.18389\n\nPreprint 2:
 https://arxiv.org/abs/2508.17778\n\n&nbsp\;
CATEGORIES:Seminars,Youtube
LOCATION:Amphi 7\, 19 Place Marguerite Perey\, Palaiseau\, France
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=19 Place Marguerite Perey\,
 Palaiseau\, France;X-APPLE-RADIUS=100;X-TITLE=Amphi 7:geo:0,0
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TZID:Europe/Paris
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DTSTART:20251026T020000
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
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