| dc.description.abstract | With the continuous development of the network environment, the current net-work performance has fallen short of expectations and there is a need for further improvement. Hence, O-RAN introduces AI/ML workflows to achieve network optimization, predictive maintenance, intelligent traffic management, security and anomaly detection, and customer experience. By integrating AI/ML workflows into O-RAN, operators can leverage the power of data-driven decision-making, automation, and optimization to create more efficient, reliable, and intelligent mobile networks. This enables them to deliver better service quality, reduce costs, and adapt to the evolving needs of mobile communication systems. To address concerns regarding trust in AI/ML models where network operators have limited control, several measures can be taken. Firstly, selecting models that offer transparency and explainability ensures operators can understand and interpret the decision-making process. Additionally, rigorous testing and validation in various scenarios, including simulated and real-world environments, help evaluate performance and reliability. Adhering to industry standards, regulations, and implementing security measures and privacy compliance also fosters trust. Collaborative development and peer reviews involving experts from different domains provide external validation. When comparing AI/ML solutions, considering vendor reputation, track record, performance metrics, and
customer reviews is crucial. Lastly, to ensure real network performance, deploying intelligent control methods in virtual environments, such as near-RT RIC’s xApp, can verify algorithm feasibility and minimize erroneous decisions. By leveraging training environments that combine expert knowledge with AI/ML techniques, operators can establish fully automated app development processes and mitigate risks associated with AI/ML deployments. | en_US |