An Open Approach to Autonomous Ran Fault Management
Shubhabrata Mukherjee, Oliver Coudert, Cory Beard
Abstract
In this article, we propose a multi-stage AI-based framework called “open Fault Management” or “openFM,” for end-to-end autonomous fault management of the open radio access network (open RAN) that is also applicable to 5G and beyond 5G next-generation networks. Open RAN is the subsequent stage of evolution for the next-generation RAN. A unified vendor-independent fault management system architecture that can be seamlessly integrated with open RAN and other parallel evolving networks is outlined in this article. An optimized combination of classical machine learning and artificial neural network (ANN)-based deep learning is implemented and analyzed in different stages of the autonomous fault management process, and their accuracy and various other metrics of performance are presented. We are able to achieve a classification accuracy of 98 percent for the prediction of false alarms and 96 percent for the prediction of alarm-specific suggestive actions. The Random-Forest-based classifier outperforms five other classifiers by being consistently accurate with a precision rate of more than 97 percent and a recall score of more than 99 percent, also with a shorter training time.