Enabling Reusable and Comparable xApps in the Machine Learning-Driven Open RAN

The advent of the Open Radio Access Network (O-RAN) specifications for 5G and 6G Radio Access Networks (RANs) has brought forth a great interest in the use of machine learning to perform control and management tasks. The integration of machine learning in the O-RAN architecture is initially envisioned to be implemented through xApps, applications that act in a near-real timescale and that have machine learning models meant for specific tasks. However, the development of machine learning-based xApps presents challenges, as although the xApp architecture facilitates component reusability for the RAN, the state-of-the-art architectures for xApps themselves require the implementation of an ad-hoc xApp for each machine learning model. Therefore, these architectures limit the reusability of the components of xApps as applications, even for xApps meant for the same purpose. To address these issues, we propose the Intelligent xApp Architecture (IxAA), a software architecture to simplify the implementation of machine learning-based xApps with a focus on reuse, easing the comparison of machine learning models. As a proof of concept, we developed xAssessment, an xApp to evaluate the performance of data prediction models. Our evaluation shows the performance results of five machine learning models predicting three different RAN metrics through xAssessment in a simulated O-RAN testbed.