Distance-Based Resource Allocation for Vehicle-to-Pedestrian Safety Communication

Abstract

Cellular Vehicle to Everything (V2X) has redefined the vehicular communication architecture as something that needs an ultra-reliable link, high capacity, and fast message delivery in vehicular networks. The V2X scenarios are broadly categorized as Vehicle to Vehicle (V2V), Vehicle to Infrastructure (V2I), Vehicle to Pedestrians (V2P), and Vehicle to Network (V2N). Vulnerable pedestrians belong to the V2P category and hence require an ultra-reliable link and a fast message delivery in case the moving vehicle is in the close proximity of the pedestrian. However, congestion in the network calls for an optimized resource allocation that would allow a fast and secure connection between a vehicle and the pedestrian. In this paper, we have proposed a distance-based resource allocation that classifies the pedestrians in different categories, performs a one-to-many weighted bipartite matching, and finally a reinforcement learning based power allocation.