AutoRAN: Automated and Zero-Touch Open RAN Systems

2025-04-15

AI Summary

To address the deployment complexity, high manual dependency, and inconsistent performance of Open RAN in multi-vendor, heterogeneous hardware, and cloud-native environments, this paper proposes the first large language model (LLM)-driven zero-touch automation framework. The framework integrates an intent translation engine—automatically mapping high-level business intents to executable configurations—telemetry-driven closed-loop optimization, infrastructure-as-code (IaC), and CI/CD pipelines, all built upon a decoupled microservices architecture compatible with leading open-source stacks including OpenAirInterface, NVIDIA ARC RAN, Open5GS, and OSC RIC. Evaluated on a hybrid x86/ARM/GPU cluster, it achieves end-to-end automated private 5G network deployment within 60 seconds, delivering 1.6 Gbps throughput while reducing human intervention by over 90%. Its performance matches that of conventional non-cloud-native deployments.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: (Large) Language Models

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

Abstract

[...] This paper presents AutoRAN, an automated, intent-driven framework for zero-touch provisioning of open, programmable cellular networks. Leveraging cloud-native principles, AutoRAN employs virtualization, declarative infrastructure-as-code templates, and disaggregated micro-services to abstract physical resources and protocol stacks. Its orchestration engine integrates Language Models (LLMs) to translate high-level intents into machine-readable configurations, enabling closed-loop control via telemetry-driven observability. Implemented on a multi-architecture OpenShift cluster with heterogeneous compute (x86/ARM CPUs, NVIDIA GPUs) and multi-vendor Radio Access Network (RAN) hardware (Foxconn, NI), AutoRAN automates deployment of O-RAN-compliant stacks-including OpenAirInterface, NVIDIA ARC RAN, Open5GS core, and O-RAN Software Community (OSC) RIC components-using CI/CD pipelines. Experimental results demonstrate that AutoRAN is capable of deploying an end-to-end Private 5G network in less than 60 seconds with 1.6 Gbps throughput, validating its ability to streamline configuration, accelerate testing, and reduce manual intervention with similar performance than non cloud-based implementations. With its novel LLM-assisted intent translation mechanism, and performance-optimized automation workflow for multi-vendor environments, AutoRAN has the potential of advancing the robustness of next-generation cellular supply chains through reproducible, intent-based provisioning across public and private deployments.

Problem

• Automating zero-touch provisioning of open RAN systems
• Translating high-level intents into machine configurations using LLMs
• Optimizing multi-vendor RAN deployment with cloud-native principles

Innovation

• Cloud-native virtualization and micro-services
• LLM-assisted intent translation mechanism
• Multi-vendor RAN automation with CI/CD