ALLSTaR: Automated LLM-Driven Scheduler Generation and Testing for Intent-Based RAN

2025-05-23

AI Summary

Existing RAN scheduling approaches for 6G AI-RAN suffer from coarse-grained network slicing, heterogeneous algorithm implementations, and a lack of systematic real-world validation. Method: This paper proposes the first LLM-driven framework for fully automated scheduler synthesis and intent alignment. Leveraging OCR-LLM joint parsing of literature, it enables natural-language intent semantic modeling, automatic extraction of scheduling algorithms, code generation, and O-RAN dApp containerization. The framework transcends slice-level constraints to support fine-grained, user-level dynamic decisions on channel allocation, caching, and service type. Contribution/Results: Evaluated on a 5G-compliant over-the-air (OTA) platform, it benchmarks 18 scheduling algorithms—establishing the first large-scale, reproducible scheduling performance spectrum. Multi-intent case studies demonstrate a 42% reduction in latency and a 31% increase in throughput.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Algorithm Configuration
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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

Problem

• Lack of dynamic adaptation in RAN scheduling for user conditions
• Heterogeneity and complexity in scheduler implementation and evaluation
• Insufficient systematic testing of schedulers in production environments

Innovation

• LLM-driven automated scheduler generation
• Intent-based scheduling with dynamic selection
• O-RAN dApps for on-the-fly deployment