# 5G V2X System-Level Architecture of 5 GCAR Project
## 3 Overall 5 GCAR Architecture Design
### Edge Computing Enhancements  
The availability of computing capabilities at the edge of the network together with an overall management of central and edge resources opens up the possibility for several improvements in mobile networks to support vehicular use cases [17]. To take full advantage of edge computing, 5 GCAR considered several enhancements from a core network perspective as well as from an access network point of view. From a core network perspective, 5 GCAR enhanced the concept of mobility support with an approach where mobility-related network procedures (i.e., handover) are synched with mobility-related \"edge\" procedures (i.e., job migration). Another enhancement introduced by 5 GCAR is related to the optimization of task execution jointly considering network and edge resources. The first enhancement is associated with mobility support. Being a highly mobile terminal by definition, a vehicle may require frequent handovers when moving at high speed on a highway, for instance. Given that an edge cloud is usually hosted on local and small size data centres designed to serve a relatively limited small number of vehicles attached to base stations in its proximity. This involves the side effect that vehicles may handover multiple times within a single journey, within access points connected to different edge cloud infrastructures. This might involve several limitations, as when migrating from one edge cloud to another also the application needs to be migrated accordingly and be updated with the appropriate internal state before the vehicle completes the handover phase. From this point, considering a deployment where edge clouds co-located at some edge UPFs are available, 5 GCAR considered enhancing handover procedures in 3 GPP by jointly considering also edge cloud re-location during mobility-related procedures. The UE is configured to constantly send some particular information to the RAN (location reporting, channel status reports, etc.) and such information is used to understand whether a handover should be triggered, in this case using, for instance, the location reporting to check whether an edge cloud closer to UE location is available (for instance, this could be achieved by using the data analytics and exposure framework of the 3 GPP system). In this scenario, when there is a signal power decrease and UE handover towards a target RAN, the RAN sends the information that the UE has moved to a new target cell to the AMF, which then starts the process of choosing a target SMF. In this case, this selection triggers the process of choosing the appropriate target edge UPF with adequate edge cloud capabilities, this happening together with the reservation of resources and transfer of data between the source and target elements. With this enhancement, edge cloud re-location is intrinsically supported directly with UPF re-selection (i.e., join network and edge mobility support). Another aspect considered by 5 GCAR is the optimization of task execution jointly considering network and edge resources in millimeter-wave scenarios. Especially when considering computation-sensitive V2X use-cases, for instance, cooperative perception, local HD map acquisition, and cooperative maneuvers, edge computation capabilities are required to locally run some tasks of relevance for the use cases. At the same time, a network connection able to satisfy bitrate, latency, and reliability requirements of communications is needed to guarantee proper connectivity towards the edge cloud. From this point of view, a joint optimization of edge cloud capabilities and mobile network resources might be beneficial. Considering, for instance, a lane merge scenario, vehicles in the area of relevance may be required to frequently send their status information (location, speed, etc.) to the associated access nodes, thus potentially introducing a high volume of data to be handled by the mobile network.