Digital Twin for Network and Industrial Operation

Digital Twin (DT) technology has emerged as a transformative paradigm across various domains, offering powerful capabilities to monitor and optimize processes prior to real-world deployment. DTs are well-suited for next-generation deployment, as well as industrial applications, in which the dynamicity and complexity of processes pose significant challenges. This study proposes a novel DT-based framework that integrates network infrastructure with shop-floor industrial operations, where robotic arms execute pick-and-place tasks and communicate with a base station (BS) using a contention-based medium access control (MAC) protocol (i.e., ALOHA and carrier sense multiple access (CSMA)) at THz frequencies. The framework aims to optimize closed-loop production systems by enabling continuous and bidirectional data exchange between the robotic arms and the BS. A centralized reinforcement learning (RL) model enables joint optimization of backoff (BO) selection and task allocation, enhancing production efficiency while preserving workflow continuity. The results prove that the proposed framework significantly outperforms state-of-the-art (SoTA) solutions for network and industrial operation performance, achieving a 55.2% reduction in average latency, a 9.8% improvement in success probability, and a 27.96% increase in processed product rate. These findings highlight the robustness and effectiveness of the proposed framework across varying operational scenarios and MAC protocols.