# 5G V2X System-Level Architecture of 5 GCAR Project
## 3 Overall 5 GCAR Architecture Design
### Edge Computing Enhancements
The utilization of millimeter-wave radio access technology from this point of view provides beneficial results for offloading the computation tasks close to the access node by using the available close edge cloud capabilities. In 5 GCAR, the total computing latency (for tasks to be offloaded) is estimated considering network aspects (transmitting tasks to the edge and receiving the outputs) and computation latency at the edge node. Please note that the above calculation can be extended to consider energy consumption as well. Consequently, 5 GCAR considered an optimization model that, according to the amount of tasks scheduled for offloading, is designed with the goal of minimizing the average energy consumption and the overall latency for task completion, where the variables to be optimized are selection of most suitable access nodes considering their current load (this is to limit upload/download time), selection of most suitable edge cloud (this is to limit computation time), access node, and vehicle's transmission power (to limit power consumption). In order to provide more details about the joint optimization, the assumptions considered in this study are now provided. It is assumed that local access nodes (e.g., BS-type RSUs) are extended with edge computing capabilities. In addition, it is assumed that access nodes and vehicles have sectored beam pattern antennas. Access nodes can get limited information about road traffic, such as traffic density, from external application servers. To optimize scheduling and resource allocation of offloading tasks from the vehicles to the associated access nodes, the following procedure is repeated whenever a vehicle triggers the off-loadingprocess by sending a request to the access node. In this example, the access node is considered as a controller that is responsible for making optimization decisions. 1. A vehicle randomly (according to a distribution) generates computing tasks, some of which are scheduled for offloading. The possible reasons for offloading include vehicle's hardware limitations, lack of real-time traffic and road data at the vehicle, etc. 2. The vehicle's computing tasks' queue is updated with new arrivals and the number of tasks scheduled for offloading. 3. Dynamic channel model between the vehicle and the access node is estimated, taking into account the vehicle's speed and distance from the access node at each time. 4. The total computing latency (for tasks to be offloaded) which consists of the following parts, is estimated. 1. Latency of task uploading from the vehicle to the access node (uplink transmission). 2. Task execution time at the access node. 3. Latency of downloading the computing result from the access node to the vehicle (downlink transmission). (Uplink and downlink transmission latencies depend on the dynamic channel model estimated in the previous step, millimetre bandwidth, transmit powers of the vehicle and the RSU, uplink interference, and noise power)
5. A model is considered for the total communication and computing energy consumption by access node and the vehicle. 6. An optimization model is considered with the objective of minimizing the average energy consumption and some constraints including latency constraint, maximum transmit powers, and stability conditions of the vehicle's computing queue. The variables to be optimized are RSU and the vehicle's transmit power, as well as the number of tasks scheduled for offloading. The output of the optimization problem determines whether a certain number of tasks can be offloaded to a certain access node.