# From Cyber-Physical Convergence to Digital Twins: A Review on Edge Computing Use Case Designs
## 9 Network Digital Twin for Vehicle-to-Edge Communication
### Edge Computing-Based Digital Twin for Open RAN
#### 9.2.2 Open RAN-Inspired Edge Digital Twin--A Vehicular Communication Use Case  
Even though the DT technology is specifically aimed for the simulation of future experiences, the calculation of context, and the development of a chain of outcomes, it can also be used in the real-time optimization of edge computing algorithms [142]. In safety-critical applications, such as autonomous driving, it can be used to create a progressively safer environment through V2E communication. For instance, for some of the challenges faced in autonomous driving such as drastic weather changes that may require a change in the route course, the edge can assist the DT to quickly compute the necessary changes that need to be made. Developing a DT of the terrain (infrastructure DT) and of the vehicle (device/vehicle DT) can help the edge computing technology in predicting and successfully executing a safe and successful journey. Therefore, in order to realize the full potential of open RAN at the edge, an edge DT (EDT) can be designed for real-time network modeling and optimization. To this effect, an EDT can be designed as a section of the NDT that is described for both pre- and post-deployment for supporting vehicular communications, designed as shown in Figure 11 below.  
As shown in Figure 11 above, a DT-enabled V2E network is realized through two closed loops between physical V2E networks and the DTs. In the computation offloading scenario, the digital models of the vehicles and of the infrastructure interact via inter-twin communication managed through the blockchain technology. Based on the current contributions in the DT technology for edge computing problems, the research designs are conducted based on the tabulation shown in Table 2 below.  
Figure 11: Digital twin implementation of a computation offloading scenario in an open edge platform capable of handling vehicular communications.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Objective** & **Technique** & **Algorithm** & **Considerations** & **Refs** \\ \hline Enabling new functionalities, e.g., hyper-connected experience and low latency & Formulate an edge & Deep reinforcement & Use of transfer learning & [143] \\ \hline Incorporate DT into wireless networks to migrate real-time data processing & Blockchain-enpowered FML framework to run in the DT for collaborative learning & Improve system reliability, security, balance, learning, accuracy, and time cost & [144] \\ \hline Ensure and learning & Real-time & C-Deep deterministic policy & Interplay between local DT edge computing on local MEC and the global one on cloud due to the nature of network states & [145] \\ \hline Develop RA model and establish a joint power optimization optimization function, delay, and unbalanced RA rate & While optimization optimization algorithm & Demonstrate that RA and allocation objective function value, power consumption, and reduce RA imbalance rates & [146] \\ \hline Address DT construction challenges and assisted resource scheduling, e.g., low accuracy and large iteration delay & Federated machine learning & Deep Q-learning network & Low-latency accurate, and secure DT to jointly optimize total iteration delay and loss function, and leverage model recognition & [147] \\ \hline Blockchain proof of authority trust mechanism to provide quality services, e.g., data security and privacy & Combine a blockchain-based distributed network with DT for IIoT & Blockchain and deterministic pseudo-random (DPRG) & Enhance the authority of decentralized DT combined blockchain networks & [148] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Application of the digital twin technology in edge computing.