Intelligent Closed-loop RAN Control with xApps in OpenRAN Gym

TL;DR

This paper explores designing AI/ML solutions for intelligent closed-loop control in Open RAN, demonstrating their implementation via xApps on the O-RAN near-real-time RIC using OpenRAN Gym, with a practical test on a cellular network emulator.

Contribution

It provides guidelines for AI/ML design in Open RAN control and demonstrates their deployment using OpenRAN Gym with a real-world test case.

Findings

High flexibility of OpenRAN Gym-based xApp development environment.

Successful deployment of an xApp on a cellular network emulator.

Effective control of network performance with AI/ML solutions.

Abstract

Softwarization, programmable network control and the use of all-encompassing controllers acting at different timescales are heralded as the key drivers for the evolution to next-generation cellular networks. These technologies have fostered newly designed intelligent data-driven solutions for managing large sets of diverse cellular functionalities, basically impossible to implement in traditionally closed cellular architectures. Despite the evident interest of industry on Artificial Intelligence (AI) and Machine Learning (ML) solutions for closed-loop control of the Radio Access Network (RAN), and several research works in the field, their design is far from mainstream, and it is still a sophisticated and often overlooked operation. In this paper, we discuss how to design AI/ML solutions for the intelligent closed-loop control of the Open RAN, providing guidelines and insights based on exemplary solutions with high-performance record. We then show how to embed these solutions into xApps instantiated on the O-RAN near-real-time RAN Intelligent Controller (RIC) through OpenRAN Gym, the first publicly available toolbox for data-driven O-RAN experimentation at scale. We showcase a use case of an xApp developed with OpenRAN Gym and tested on a cellular network with 7 base stations and 42 users deployed on the Colosseum wireless network emulator. Our demonstration shows the high degree of flexibility of the OpenRAN Gym-based xApp development environment, which is independent of deployment scenarios and traffic demand.