X5G: An Open, Programmable, Multi-vendor, End-to-end, Private 5G O-RAN Testbed with NVIDIA ARC and OpenAirInterface

2024-06-22

arXiv.org

AI Summary

To address the challenge of simultaneously achieving programmability, high performance, and end-to-end integrability in private 5G networks, this project develops the first open-source, 8-node programmable O-RAN end-to-end testbed. It integrates NVIDIA Aerial (GPU-accelerated PHY), OpenAirInterface (L2/L3 protocol stack), and a near-real-time RIC, with hardware-software co-design enabled via SCF FAPI and E2 interfaces. Key contributions include: (1) a multi-vendor heterogeneous hardware-software co-design architecture; (2) the first joint deployment of GPU-accelerated PHY and OSC-compliant near-real-time RIC in O-RAN; and (3) a digital twin–based RF environment planning framework. Experimental results demonstrate a peak downlink throughput of 1.65 Gbps and uplink of 143 Mbps per cell, supporting concurrent operation of four commercial CPEs and 25 UEs. End-to-end evaluations using iPerf and video streaming confirm high throughput, low latency (<10 ms), and robust performance under dynamic channel conditions.

Technology Category

• Planning, Routing, and Scheduling: Plan Execution and Monitoring
• Machine Learning: Hardware-aware ML
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Security and Privacy: Browser and end-point security
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

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.

Problem

• Enable software-based 5G deployments with high performance.
• Integrate hardware acceleration for scalable PHY layer performance.
• Develop programmable RAN elements and intelligent edge controllers.

Innovation

• GPU-accelerated PHY layer using NVIDIA Aerial SDK
• Integration of OpenAirInterface with SCF FAPI
• Near-real-time RAN Intelligent Controller (RIC)