Learning-based RSU Placement for C-V2X with Uncertain Traffic Density and Task Demand

In the 3GPP-based cellular vehicle-to-everything (C-V2X) architecture, the Roadside Units (RSU) plays an important role for the enhancement of Quality of Service (QoS) of the vehicular applications. The placement of RSUs has been studied in the literature. However, existing works assume known road traffic distribution with given task demands, which is a simplification of the complex real world situation. In this work, we investigate the optimum RSU placement for C-V2X with uncertain traffic density and task demands. We formulate this RSUs Placement in C-V2X Network (RPCN) problem to minimize the expected vehicle tasks offloading delay through uncertain programming where vehicles positions and tasks are treated as arbitrary stochastic variables. We propose a learning-based algorithm by integrating Stochastic Simulation (SS), Artificial Neural Network (ANN) and meta-heuristic algorithm to determine the placement from real traffic data. The proposed method is an offline design with high practicability. We conducted intensive real-trace driven simulations to demonstrate the effectiveness of our approach on placing RSUs with lower task offloading delay.