AI & ML
ONDT: A Modular and Virtualized O-RAN Digital Twin for xApp/rApp Closed-Loop Experimentatio
arXiv cs.NI0 views
arXiv:2610.07889v1 Announce Type: new Abstract: Open Radio Access Network (O-RAN) architectures extend programmability in next-generation cellular networks through Near-Real-Time and Non-Real-Time RAN Intelligent Controllers (RICs), enabling AI/ML-driven xApps and rApps to optimize network behavior dynamically. However, validating automated closed-loop control algorithms in live environments presents significant operational risks and deployment costs. To address this challenge, this paper introduces the O-RAN Digital Twin (ONDT), a modular, virtualized, and cloud-native experimentation framework developed as a 6G enabler within the SNS SUNRISE-6G project. We present the ONDT architecture and its end-to-end experimentation workflows, showing how the framework automates O-RAN-compliant rApp/xApp onboarding, RAN scenario configuration and initialization, Key Performance Metric (KPM) collection, and closed-loop control validation. Experimental validation using Juniper RIC platforms and the Keysight RICtest emulation framework demonstrates ONDT ability to provide an isolated and reproducible environment for KPM monitoring, cell ON/OFF control, automated dataset generation, and rApp/xApp validation.
Source:https://arxiv.org/abs/2610.07889PreviousWhen Weak Reports Matter: Staged Anchored Fusion for Cooperative UAV SensingNextIEEE 802.11bx - WLAN Intelligent Networking (WIN): Toward an AI-Ready Wi-Fi 9
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