The Role of Digital Twin in 6G-Based URLLCs: Current Contributions, Research Challenges, and Next Directions

Abstract

Substantial improvements in the area of ultra reliable and low-latency communication (URLLC) capabilities, as well as possibilities of meeting the rising demand for high-capacity and high-speed connectivity are expected to be achieved with the deployment of next generation 6G wireless communication networks. This thank to the adoption of key technologies such as unmanned aerial vehicles (UAVs), reflective intelligent surfaces (RIS), and mobile edge computing (MEC), which hold the potential to enhance coverage, signal quality, and computational efficiency. However, the integration of these technologies presents new optimization challenges, particularly for ensuring network reliability and maintaining stringent latency requirements. The Digital Twin (DT) paradigm, coupled with artificial intelligence (AI) and deep reinforcement learning (DRL), is emerging as a promising solution, enabling real-time optimization by digitally replicating network devices to support informed decision-making. This paper reviews recent advances in DT-enabled URLLC frameworks, highlights critical challenges, and suggests future research directions for realizing the full potential of 6G networks in supporting next-generation services under URLLCs requirements.