Digital Twin Assisted Cross-Layer Resource Scheduling in ORAN System

The open radio access network (O-RAN) architecture is a promising RAN virtualization solution which provides interfaces for various timescale RAN intelligent control (RIC) schemes, thus achieving AI-based, cross layer resource scheduling. However, obtaining sufficient data to train AI models is difficult and costly. Digital twin (DT) technology can create high fidelity virtual world, which can not only be used to generate high-quality training data, but also provide an interactive platform for trial and error algorithms such as reinforcement learning (RL) to reduce the cost of interacting with real systems. Therefore, in this paper, we introduce our scheme that integrating DT and RL techniques to achieve cross layer resource scheduling in O-RAN system. We focus on the following two issues: i) how to reduce the gap between virtual and reality, ii) how to continuously evolve AI models to cope with unseen scenarios, and our preliminary ideas are also presented.