The evolution toward open, programmable O-RAN and AI-RAN 6G networks creates unprecedented opportunities for Intent-Based Networking (IBN) to dynamically optimize RAN[...]. However, applying IBN effectively to the RAN scheduler [...] remains a significant challenge. Current approaches predominantly rely on coarse-grained network slicing, lacking the granularity for dynamic adaptation to individual user conditions and traffic patterns. Despite the existence of a vast body of scheduling algorithms [...], their practical utilization is hindered by implementation heterogeneity, insufficient systematic evaluation in production environments, and the complexity of developing high-performance scheduler implementations.[...] To address these limitations, we propose ALLSTaR (Automated LLm-driven Scheduler generation and Testing for intent-based RAN), a novel framework leveraging LLMs for automated, intent-driven scheduler design, implementation, and evaluation. ALLSTaR interprets NL intents, automatically generates functional scheduler code from the research literature using OCR and LLMs, and intelligently matches operator intents to the most suitable scheduler(s). Our implementation deploys these schedulers as O-RAN dApps, enabling on-the-fly deployment and testing on a production-grade, 5G-compliant testbed. This approach has enabled the largest-scale OTA experimental comparison of 18 scheduling algorithms automatically synthesized from the academic literature. The resulting performance profiles serve as the input for our Intent-Based Scheduling (IBS) framework, which dynamically selects and deploys appropriate schedulers that optimally satisfy operator intents. We validate our approach through multiple use cases unattainable with current slicing-based optimization techniques, demonstrating fine-grained control based on buffer status, physical layer conditions, and heterogeneous traffic types

翻译：开放、可编程的O-RAN与AI-RAN 6G网络的发展，为意图驱动网络动态优化无线接入网创造了前所未有的机遇。然而，将意图驱动网络有效应用于无线接入网调度器仍面临重大挑战。现有方法主要依赖粗粒度的网络切片，缺乏根据个体用户状态和流量模式进行动态适配的细粒度能力。尽管存在大量调度算法研究成果，其实用化进程仍受限于实现异构性、生产环境中系统性评估不足以及开发高性能调度器实现的复杂性。为突破这些局限，我们提出ALLSTaR——一种基于大语言模型的创新框架，实现意图驱动的自动化调度器设计、实现与评估。ALLSTaR能够解析自然语言意图，通过光学字符识别与大语言模型从研究文献中自动生成功能性调度器代码，并智能地将运营商意图匹配至最合适的调度器。我们的实现方案将这些调度器部署为O-RAN分布式应用，支持在生产级5G合规测试平台上进行实时部署与测试。该方法实现了对18种从学术文献自动合成的调度算法进行最大规模的空口实验比较。生成的性能画像将作为意图驱动调度框架的输入，动态选择并部署能最优满足运营商意图的调度器。我们通过多个现有切片优化技术无法实现的用例验证了该方案，展示了基于缓冲区状态、物理层条件和异构流量类型的细粒度控制能力。