# 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 Traditional RAN  
Consider a cellular vehicle-to-edge network where a two-tier vehicular network on a one-dimensional road segment is considered. Here, the first tier consists of the macro-cell with a gNB, while the second tier consists of a set \(\mathcal{N}=\{1,2,\cdot\cdot\cdot,N\}\) of small cells, each one having a single roadside unit (RSU), \(RSU_{n}\), as shown in Figure 9 below. Figure 8: A digital twin-driven edge intelligent real-time predictive maintenance technique. In Figure 9 above, the gNB serves as a controller that manages the RSUs on the second tier by controlling their resources and also establishing offloading and computing policies. The RSUs are assumed to be connected to edge servers using the evolved common public radio interface (eCPRI) front-haul interfaces [136] to provide delay-constrained computing services. As it can be seen in Figure 9 above, a set \(\mathcal{K}=\{1,2,\cdots,K\}\) of vehicles are randomly distributed on a road segment, and each one of them periodically generates computing tasks that require results to the RSU via an offloading link characterized by \(\gamma_{nk}\). Due to the safety-critical nature of vehicular communications, this is a site-specific DT for obtaining time-variant link behavior in terms of packet loss rates (PLR) for analyzing the reliability of vehicular communication scenarios. In this application, the use of a real-time geometry-based stochastic channel model, which was studied in [102], can be adopted in simulating doubly selective channel frequency responses. Consider a scenario whereby the best offloading decision in a vehicle-to-edge (V2E) network needs to be found. This offloading decision that is sought must associate each tasks that is generated by vehicles with one of the available edge servers. Assuming that the objective goal of minimizing (i) the offloading error, (ii) processing latency, (iii) overall energy consumption, (iv) or a combination of these objectives, the question is how a perfect DT can be designed to solve this problem in the best possible way. One possible way is to split the DT design into two, as discussed below. * Device/vehicle DT: The vehicle DT (vDT) can be thought of as the profile of the device, which may include (i) the travelling speed, (ii) the trajectory and real-time vehicle location, as well as (iii) the resource requirements. 1. The requirements: The requirements may include (i) the size of the task (payload size), (ii) the required processing cycles, as well as (iii) the dependency and the priority among tasks. 2. The AI agent: The AI agent then iteratively associates the device tasks with the servers and takes note of the reduction of the objective, i.e., records the resulting reward. The features that describe the status of the server as well as the device requirements are then fed to learn the DNN, which uses its generalization capabilities to yield general scheduling policies. In relation to the other DTs, the vehicle DT monitors the vehicle trajectory and vehicle speed optimization--to name a few. Other additional parameters that can be monitored are blind-spot and accident detection. * Infrastructure DT: In edge computing, the infrastructure consists of (i) the RSUs, (ii) the edge server, and (iii) the vehicles themselves. The responsibility of the infrastructure DT (iDT) is monitoring and optimization of service provisioning to the devices. This means that the infrastructure DT manages device behavior in terms of their real  
Figure 9: A network digital twin model of a multi-link communication in a cellular vehicle-to-edge offloading scenario. time location and network behavior. Then, the state of each edge server can be described using its resources, i.e., (i) its computing speed and (ii) the quality of the communication channel. For an efficient infrastructure DT establishment, effective mapping between RSUs and vehicles must be established. 1.