Optimizing Vehicle-to-Vehicle (V2V) Communication Efficiency with KNN-based Dynamic Time slot Allocation

In the continually evolving field of vehicular communications, the efficient allocation of time slots for Vehicle-to-Vehicle (V2V) communication is of utmost importance. This work introduces a novel approach that employs a K-Nearest Neighbors (KNN) algorithm to allocate time slots in NR V2X sidelink communications. The allocation is specifically tailored to dynamically adjust to the current density of traffic in real-time. The efficacy of the KNN-based time slot allocation system is assessed through simulating the V2X environment, encompassing a fleet of vehicles. The efficacy of the suggested methodology is assessed in relation to a random allocation technique and it is observed that the suggested strategy notably enhances allocation efficiency, slot utilization, contention window, and communication time. The findings illustrate the capacity of a KNN-based dynamic allocation system to adapt to different levels of traffic congestion, leading to the efficient utilization of network resources and the mitigation of communication latency. Furthermore, the examination of the implementation of priority queues in the allocation process is explored, resulting in enhanced efficiency of the system in scenarios involving a substantial amount of traffic.