Abstract:The open radio access network (O-RAN) RAN intelligent controller (RIC) hosts data-driven xApps and rApps to optimize network performance. However, two challenges hinder ML-driven xApp/rApp development: (i) key performance metric (KPM) data scarcity caused by interface latency, and (ii) network disruption risks when testing and validating AI models directly on live networks. We develop OpenTwin, a digital twin framework built on an open-source O-RAN simulator (ns-O-RAN-flexRIC) and KPM streaming via the O1 interface, deployed within the non-RT RIC. OpenTwin uses a two-step ML approach: an XGBoost model that learns time-varying network behavior to generate simulator configuration parameters, followed by a time-aware recursive least squares (RLS) tuner that continuously corrects KPM deviations between the twin and real-world measurements. A deviation-aware scoring mechanism monitors twin fidelity and automatically triggers resynchronization upon detecting network drift. We demonstrate OpenTwin with an energy-saving xApp that validates control policies in the virtual space before applying reconfigurations to the physical network. Experimental results show that OpenTwin mirrors real-world KPMs with up to 96% accuracy and enables the xApp to significantly reduce energy consumption without disrupting live operations.
| Comments: | 11 pages, 2 tables, 6 figures |
| Subjects: | Networking and Internet Architecture (cs.NI) |
| Cite as: | arXiv:2605.24662 [cs.NI] |
| (or arXiv:2605.24662v1 [cs.NI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.24662 arXiv-issued DOI via DataCite (pending registration) |
From: Md Sharif Hossen [view email]
[v1]
Sat, 23 May 2026 17:04:12 UTC (2,562 KB)
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