X5G: An Open, Programmable, Multi-vendor, End-to-end, Private 5G O-RAN Testbed with NVIDIA ARC and OpenAirInterface
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Abstract (arXiv)
As Fifth generation (5G) cellular systems transition to softwarized, programmable, and intelligent networks, it becomes fundamental to enable public and private 5G deployments that are (i) primarily based on software components while (ii) maintaining or exceeding the performance of traditional monolithic systems and (iii) enabling programmability through bespoke configurations and optimized deployments. This requires hardware acceleration to scale the Physical (PHY) layer performance, programmable elements in the Radio Access Network (RAN) and intelligent controllers at the edge, careful planning of the Radio Frequency (RF) environment, as well as end-to-end integration and testing. In this paper, we describe how we developed the programmable X5G testbed, addressing these challenges through the deployment of the first 8-node network based on the integration of NVIDIA Aerial RAN CoLab Over-the-Air (ARC-OTA), OpenAirInterface (OAI), and a near-real-time RAN Intelligent Controller (RIC). The Aerial Software Development Kit (SDK) provides the PHY layer, accelerated on Graphics Processing Unit (GPU), with the higher layers from the OAI open-source project interfaced with the PHY through the Small Cell Forum (SCF) Functional Application Platform Interface (FAPI). An E2 agent provides connectivity to the O-RAN Software Community (OSC) near-real-time RIC. We discuss software integration, network infrastructure, and a digital twin framework for RF planning. We then profile the performance with up to 4 Commercial Off-the-Shelf (COTS) smartphones for each base station with iPerf and video streaming applications, as well as up to 25 emulated User Equipments (UEs), measuring a cell rate higher than 1.65 Gbps in downlink and 143 Mbps in uplink.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Jun 2024.
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Citing papers per year: 2025: 1; 2026: 5.
6 papers.
- 2025Beyond Connectivity: An Open Architecture for AI-RAN Convergence in 6Gcited by 10
- 2026GENESIS: Harnessing AI Agents for Autonomous 6G RAN Synthesis, Research, and Testingcited by 2
- 2026LOLLA: Deep Reinforcement Learning for Closed-Loop Link Adaptation Towards a GPU-Accelerated AI-RANcited by 1
- 2026AUGUSTE: Online-Learning dApp for Predictive URLLC Schedulingcited by 0
- 2026AI-RAN on NPUs: Baseband Processing Without Baseband Chipscited by 0
- 2026Improving 5G AI-RAN MCS Selection by Predicting Retransmissionscited by 0
- 2026Improving 5G AI-RAN MCS Selection by Predicting Retransmissions8 Sep 2026
- 2026AI-RAN on NPUs: Baseband Processing Without Baseband Chips5 Jul 2026
- 2026LOLLA: Deep Reinforcement Learning for Closed-Loop Link Adaptation Towards a GPU-Accelerated AI-RAN22 Jun 2026
- 2026AUGUSTE: Online-Learning dApp for Predictive URLLC Scheduling2 Jun 2026
- 2026GENESIS: Harnessing AI Agents for Autonomous 6G RAN Synthesis, Research, and Testing26 May 2026
- 2025Beyond Connectivity: An Open Architecture for AI-RAN Convergence in 6G9 Jul 2025