5G-CT: Automated Deployment and Over-the-Air Testing of End-to-End Open Radio Access Networks
Year: 2024
Type: article
Abstract: Deploying and testing cellular networks is a complex task due to the multitude of components involved from the core to the radio access network (RAN) and user equipment (UE), all of which requires integration and constant monitoring. Additional challenges are posed by the nature of the wireless channel, whose inherent randomness hinders the repeatability and consistency of the testing process. Consequently, existing solutions for both private and public cellular systems still rely heavily on human intervention for operations such as network reconfiguration, performance monitoring, and end-to-end testing. This reliance significantly slows the pace of innovation in cellular systems. To address these challenges, we introduce 5G-CT, an automation framework based on OpenShift and the GitOps workflow, capable of deploying a soft-warized end-to-end 5G and O-RAN-compliant system in a matter of seconds without the need for any human intervention. We have deployed 5G-CT to test the integration and performance of open-source cellular stacks, including OpenAirInterface, and have collected months of automated over-the-air testing results involving software-defined radios. 5G-CT brings cloud-native continuous integration and delivery to the RAN, effectively addressing the complexities associated with managing spectrum, radios, heterogeneous devices, and distributed components. Moreover, it endows cellular networks with much needed automation and continuous testing capabilities, providing a platform to evaluate the robustness and resiliency of Open RAN software.
Source: IEEE Communications Magazine
Institution: Northeastern University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: CCRI: Grand: Colosseum: Opening and Expanding the World's Largest Wireless Network Emulator to the Wireless Networking Community, AI Institute for Future Edge Networks and Distributed Intelligence (AI-EDGE), SpecEES: DISCOVER: Device Identification for Spectrum-optimization using COnVolutional nEural netwoRks, G5259331294, G56008422, G5664237557, G608057831, G6984691565, G8876996369
OpenAlex ID: W4402129365