NEAT-Based Resource Allocation for Emergency Service Provisioning in C-V2X Networks

The rise of vehicular networks has ushered in the era of vehicle-to-everything (V2X) communication aimed at bolstering driving safety. A pivotal aspect of V2X communication is its role in facilitating emergency warning systems. This study focuses on the propagation of emergency messages and the provisioning of emergency services through V2X communication while considering the efficient allocation of resources necessary for effective vehicle communication. The primary objective of resource allocation within the context of Cellular-Vehicle-to-Everything (C-V2X) is to optimize the utilization of available resources, amplify system capacity, and address the diverse communication requisites within the confines of system constraints. A notable challenge in C-V2X resource allocation resides in the judicious allocation of spectrum resources and broadcast opportunities to V2X users. Therefore, we present NeuroEvolution of Augmenting Topologies (NEAT)-based efficient channel allocation within the C-V2X framework. Our approach aims to maximize the sum rate and throughput of emergency service vehicles (ESV) while ensuring the attainment of a minimum threshold throughput for other vehicles. Subsequently, we conduct a comparative analysis between the average sum rate achieved by the NEAT algorithm and a random resource allocation scheme. Furthermore, we undertake a comparative assessment of the time complexity of NEAT in contrast to other state-of-the-art techniques employed for channel allocation. These techniques encompass the graph matching algorithm, the Hungarian, and the brute force method. Our proposed C-V2X model demonstrates superior performance across various evaluation metrics compared to various alternative algorithms.