Digital Twin of Open Radio Access Network

Summary

The Open Radio Access Network (ORAN) architecture provides foundational blocks to build integration of AI and ML in access networks to transform data into intelligence in future wireless networks. The ORAN architecture can leverage ML and AI techniques within xApps and rApps to autonomously analyze network data, learning from interactions with the environment to make intelligent decisions to optimize network performance. xApps and rApps are software applications designed to operate within Near real-time (Near-RT) RIC and non-real-time (non-RT) RIC components of the O-RAN architecture, respectively. While ORAN provides essential ingredients of AI-native RAN, ML deployment in live networks presents a significant risk of deterioration of quality of experience. Digital Twin of the network in this case are essential to analyze, predict and update network operations and solutions to meet the real time requirements of the network. This project will study the modelling and analysis of digital twins for real time resource optimization and performance prediction and evaluation.

The Open Radio Access Network (ORAN) architecture provides foundational blocks to build integration of AI and ML in access networks to transform data into intelligence in future wireless networks. The ORAN architecture can leverage ML and AI techniques within xApps and rApps to autonomously analyze network data, learning from interactions with the environment to make intelligent decisions to optimize network performance. xApps and rApps are software applications designed to operate within Near real-time (Near-RT) RIC and non-real-time (non-RT) RIC components of the O-RAN architecture, respectively. While ORAN provides essential ingredients of AI-native RAN, ML deployment in live networks presents a significant risk of deterioration of quality of experience. Digital Twin of the network in this case are essential to analyze, predict and update network operations and solutions to meet the real time requirements of the network. This project will study the modelling and analysis of digital twins for real time resource optimization and performance prediction and evaluation.

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