# Intelligent Computation Offloading Based on Digital Twin-Enabled 6G Industrial IoT
## 1 Introduction  
Following the initial commercial implementation of 5G networks, industry and academic organizations have carried out their work to the architecture, requirements, and applications of 6G mobile networks. The international telecommunication union (ITU) released a white paper on the 6G [1] vision and candidate technologies in June 2021. As the next generation communication technology, the direction of 6G technology research and development has become more and more clear [2]. The industrial internet of things (IIoT) is an intelligent manufacturing model that integrates the sensors, networking technologies, and data analytics into the industrial production process. The rapid development of artificial intelligence (AI) technology has brought an explosive growth of industrial data, and the large amount of data generated during the industrial production process is a challenge for the IIoT [3; 4; 5]. How to effectively collect, analyze, and process these data, and extract the guiding recommendations for industrial production from them is the core of the design. Realizing such rigorous and diverse service requirements requires the design of a special-purpose IIoT network that incorporates a large number of specialized functions and technical support. This is exactly what 6G technology is trying to do today [6; 7]. This innovative architecture of the 6G IIoT network compensates for the shortcomings of previous wireless network technology applied to industrial field environments, providing higher security, reliability, and real-time performance. However, due to the limited computing resources and processing capabilities of IIoT devices, it presents serious challenges for running computationally intensive applications locally. In order to meet the ever-increasing computing demands, mobile edge computing hasbeen proposed as a promising approach that deploys cloud-like computing resources at the edge of the network to provide real-time computing services to nearby IoT devices [8; 9; 10]. The system can offload whole or part of the industrial computing tasks generated on the IIoT devices to the edge servers, utilizing superior computing resources to accomplish the tasks [11]. Computation offloading is a promising approach, but it faces a number of challenges. Firstly, the computing tasks generated by IIoT devices are heterogeneous, such as the amount of data, task priority, required resources, and so on [12]. Secondly, the computation offloading decision needs to sense the network condition in real time and dynamically select the appropriate edge server [13]. Finally, how to optimize the offloading decision online to maximize the system performance is also a problem to be solved. The emergence of digital twin (DT) technology can well meet these challenges. Terminology for digital twins was first presented by [PERSON] [14] of the University of Michigan as the \"Information Mirroring Model\", which later evolved into the term \"digital twin\". Digital twins are virtual representations of the state of a physical entity or system by collecting data from physical entities through sensors and digitizing them to create a highly simulated virtual model [15; 16; 17; 18]. Digital twins are theories and technologies with universal applicability, which can be applied to many fields, especially in system design, product manufacturing, data analysis, and engineering construction, etc. [19; 20]. In view of the above characteristics, a number of studies combining DT and edge computing have recently emerged. Ref. [21] focused on minimizing the computational offload latency of DT wireless edge networks in IIoT environments through ultra-reliable and low-latency communication links. Ref. [22] proposed a mobile edge computing framework based on network digital twins that would enable intelligent resource management. Ref. [23] studied the digital twin service caching and request routing problem along with fairness awareness.