چكيده لاتين
In vehicular communications, resource collision control is of paramount importance due to its direct impact on road safety. Inadequate control can lead to communication interference, loss of critical information, and delays in the transmission of safety messages. Therefore, reducing resource collisions is a key research priority in this field. This study focuses on improving resource collision control methods in cellular vehicle-to-everything (C-V2X) communications. Given the importance of this domain in enhancing the safety and efficiency of intelligent transportation systems, a clustering-based method is proposed. This method takes into account long-term trajectory prediction and driversʹ behavioral patterns to address the issue of resource collisions in both urban and highway environments. A novel approach for resource collision control is presented in this study. The proposed method is based on a software-defined network (SDN) architecture. Vehicles are clustered based on predicted trajectories and driver behavioral patterns. More specifically, vehicles that share the highest similarity in their routes and driving behavior are grouped into the same cluster. Within each cluster, two vehicles are selected as the primary and backup cluster heads. This dual selection of cluster heads increases the cluster’s stability and maintains its structure in the event of sudden changes in vehicle movement. Resource allocation occurs in two modes: independent and controlled. In the independent mode, the cluster head selects a resource set by measuring the received signal strength and distributes these resources among the cluster members using the Semi-Persistent Scheduling (SPS) algorithm, considering future interference. In the controlled mode, a controller selects the resource set for each cluster and, along with a list of future interferences, sends it to the primary cluster head through the base station. The primary cluster head then allocates resources to the cluster members based on this list. To manage congestion and reduce signaling overhead, only the primary cluster head communicates with the base station. The proposed method demonstrates high flexibility and efficiency in both urban and highway environments and is compatible with LTE and 5G technologies. It supports both independent and controlled resource allocation modes, enabling optimal resource distribution under various conditions. Simulation results on highways with varying numbers of vehicles and resources show a significant improvement in the performance of the proposed algorithm. Compared to the benchmark solution, the proposed method achieves average improvements of 17%, 18%, and 5% in packet reception rate and error rate under low, medium, and high congestion, respectively. Overall, a 14% average improvement was observed across all conditions. However, in terms of packet block rate, the method only provides a slight improvement when sufficient resources are available. Simulation results in urban scenarios with a 300-meter awareness range considered the most challenging conditions—demonstrate that the average packet reception rate under low, medium, and high congestion is 40%, 36%, and 33%, respectively. These findings indicate that the proposed method has performed effectively even in the most demanding urban environments, contributing positively to the safety and stability of critical communications
