Operation-aware Digital Twin for Improving Performance and Sustainability of AI-driven Radio Access Networks

Digital Twin (DT) technology has emerged as an important enabler for enhancing cellular network operations, particularly in complex environments such as transportation hubs. However, existing DT efforts for cellular network often focus on modeling of physical propagation channel and communication protocols. Many of them lack a systematic approach to model how cellular network is affected by system-specific operational states (e.g., flight arrival schedule or various possible faults in an airport). In this work, we propose an operation-aware digital twin framework that incorporates the knowledge of system operations into the optimization of AI-driven radio access networks. This framework integrates a "what-if" operational scenario generator to enumerate diverse operational scenarios. Furthermore, it can sync up with the actual system’s operation states in near real time to provide on-the-fly fine-tuning of AI/ML models. We developed an end-to-end prototype of our proposed framework and validated its benefits through performance evaluation using a set of AI/ML rApps running on RAN Intelligent Controller. Our proposed framework can help advance DT technology for its use in 5G networks and beyond, enabling intelligent and responsive network management in dynamic environments.