Colosseum: The Open RAN Digital Twin
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Abstract (arXiv)
Recent years have witnessed the Open Radio Access Network (RAN) paradigm transforming the fundamental ways cellular systems are deployed, managed, and optimized. This shift is led by concepts such as openness, softwarization, programmability, interoperability, and intelligence of the network, all of which had never been applied to the cellular ecosystem before. The realization of the Open RAN vision into practical architectures, intelligent data-driven control loops, and efficient software implementations, however, is a multifaceted challenge, which requires (i) datasets to train Artificial Intelligence (AI) and Machine Learning (ML) models; (ii) facilities to test models without disrupting production networks; (iii) continuous and automated validation of the RAN software; and (iv) significant testing and integration efforts. This paper poses itself as a tutorial on how Colosseum - the world's largest wireless network emulator with hardware in the loop - can provide the research infrastructure and tools to fill the gap between the Open RAN vision, and the deployment and commercialization of open and programmable networks. We describe how Colosseum implements an Open RAN digital twin through a high-fidelity Radio Frequency (RF) channel emulator and end-to-end softwarized O-RAN and 5G-compliant protocol stacks, thus allowing users to reproduce and experiment upon topologies representative of real-world cellular deployments. Then, we detail the twinning infrastructure of Colosseum, as well as the automation pipelines for RF and protocol stack twinning. Finally, we showcase a broad range of Open RAN use cases implemented on Colosseum, including the real-time connection between the digital twin and real-world networks, and the development, prototyping, and testing of AI/ML solutions for Open RAN.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Apr 2024.
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Citing papers per year: 2024: 1; 2025: 4; 2026: 3.
8 papers.
- 2025How to Bridge the Sim-to-Real Gap in Digital Twin-Aided Telecommunication Networkscited by 6
- 2025Uncertainty Quantification and Data Efficiency in AI: An Information-Theoretic Perspectivecited by 2
- 2026SIA: Symbolic Interpretability for Anticipatory Deep Reinforcement Learning in Network Controlcited by 2
- 2025LibIQ: Toward Real-Time Spectrum Classification in O-RAN dAppscited by 1
- 2025AIRMap: AI-Generated Radio Maps for Wireless Digital Twinscited by 1
- 2024TIMESAFE: Timing Interruption Monitoring and Security Assessment for Fronthaul Environmentscited by 0
- 2026ML-Based Real-Time Downlink Performance Prediction in Standalone 5G NR Using Smartphonescited by 0
- 2026Should I Use This Synthetic Dataset for Training? How to Test with Minimal Real Datacited by 0
- 2026Should I Use This Synthetic Dataset for Training? How to Test with Minimal Real Data28 Aug 2026
- 2026ML-Based Real-Time Downlink Performance Prediction in Standalone 5G NR Using Smartphones19 Mar 2026
- 2026SIA: Symbolic Interpretability for Anticipatory Deep Reinforcement Learning in Network Control29 Jan 2026
- 2025Uncertainty Quantification and Data Efficiency in AI: An Information-Theoretic Perspective4 Dec 2025
- 2025AIRMap: AI-Generated Radio Maps for Wireless Digital Twins28 Oct 2025
- 2025How to Bridge the Sim-to-Real Gap in Digital Twin-Aided Telecommunication Networks9 Jul 2025
- 2025LibIQ: Toward Real-Time Spectrum Classification in O-RAN dApps15 May 2025
- 2024TIMESAFE: Timing Interruption Monitoring and Security Assessment for Fronthaul Environments17 Dec 2024