From the beginning, one of the key aspects of the Open RAN was to embed intelligence into the RAN natively. To this end, AI/ML plays a crucial role in the process.
Some of the goals for AI/ML within radio access networks are: decreasing the manual effort of going through large data amounts to diagnose issues and make decisions, or predicting the future to take proactive actions – thus saving time and cost.
AI/ML-based algorithms may be used, e.g., in network security applications for anomaly detection, prediction of radio resources utilization, hardware failure prediction, parameters forecasting for energy-saving purposes, or conflict detection between xApps. This is being addressed from the beginning within O-RAN ALLIANCE.
In this post, I’m discussing the overall framework for machine learning within O-RAN, touching upon the architectural aspects related to Open RAN.
At Rimedo Labs, we are recently focusing our development (more than before) using ML tools, e.g. for the Beam-Mobility Management xApp, for Signalling Storm Detection, and for Energy Saving solutions.