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Softwarization, programmable network control and the use of all-encompassingcontrollers acting at different timescales are heralded as the key drivers forthe evolution to next-generation cellular networks. These technologies havefostered newly designed intelligent data-driven solutions for managing largesets of diverse cellular functionalities, basically impossible to implement intraditionally closed cellular architectures. Despite the evident interest ofindustry on Artificial Intelligence (AI) and Machine Learning (ML) solutionsfor closed-loop control of the Radio Access Network (RAN), and several researchworks in the field, their design is far from mainstream, and it is still asophisticated and often overlooked operation. In this paper, we discuss how todesign AI/ML solutions for the intelligent closed-loop control of the Open RAN,providing guidelines and insights based on exemplary solutions withhigh-performance record. We then show how to embed these solutions into xAppsinstantiated on the O-RAN near-real-time RAN Intelligent Controller (RIC)through OpenRAN Gym, the first publicly available toolbox for data-driven O-RANexperimentation at scale. We showcase a use case of an xApp developed withOpenRAN Gym and tested on a cellular network with 7 base stations and 42 usersdeployed on the Colosseum wireless network emulator. Our demonstration showsthe high degree of flexibility of the OpenRAN Gym-based xApp developmentenvironment, which is independent of deployment scenarios and traffic demand.

Intelligent Closed-loop RAN Control with xApps in OpenRAN Gym