# Priority-Driven Resource Allocation with Reuse for Platooning in 5G Vehicular Network
## 2 Related Work  
Ensuring real-time communication reliability for platooning is critical due to the close proximity of vehicles within the platoon. The communication delay between vehicles must be minimized. According to 3 GPP standards, platooning demands lower communication latency and higher reliability compared with other vehicular communications [9]. Packet transmission in vehicle-to-vehicle communication involves two key processes: the physical transmission of packets and the request and allocation of communication resources necessary for transmission [13]. For platoon vehicles, since communication predominantly occurs between vehicles spaced only a few meters apart, interference is minimized, allowing for more efficient communication with reduced latency during the actual packet transmission process. Therefore, the timely allocation of resources to platoon vehicles is crucial for minimizing delay. (1) Traditional resource allocation algorithm: Traditional resource allocation algorithms, such as the Max-C/1 algorithm, allocate resources to the vehicle with the best channel conditions, and the Round Robin algorithm allocates resources sequentially to all vehicles, ensuring fairness but often at the cost of overall efficiency [14]. However, under conditions of network congestion, due to high vehicle density or increased communication demand, it is difficult to guarantee the necessary resources for each vehicle [14]. When resources are insufficient, vehicles must request resources again for communication, and once the maximum number of resource request attempts is reached, resource allocation is unsuccessful, requiring the vehicle to re-establish a connection with the base station and restart the process [15]. This results in additional communication delays. (2) Reinforcement learning-based resource allocation algorithm: Recently, reinforcement learning has been extensively applied in the study of resource allocation algorithms for platoon vehicles, with the goal of minimizing interference and enhancing spectral efficiency by optimizing resource allocation [16; 17; 18; 19; 20; 21]. However, reinforcement learning requires large datasets and considerable computational resources, with a training process that is time-consuming [22]. This may result in network performance degradation during the initial learning phase and could potentially affect overall network stability. In dynamic environments such as vehicular networks, continuously modeling the evolving system states in a reinforcement learning-based system is particularly challenging, which could lead to potential performance degradation in real-world scenarios [23]. [PERSON] et al. addressed the multi-objective resource allocation problem, focusing on maximizing the transmission success rate and Mean Opinion Score (MOS) of V2I communication links in a multi-platoon vehicular network using a reinforcement learning-based algorithm [16]. However, their simulation environment considered only platoon vehicles, excluding non-platoon general vehicles. [PERSON] et al. tackled the joint optimization of platoon formation, power control, and spectrum allocation in a C-V2X network, considering uncertain channel parameters [17]. Their goal was to ensure reliable intra-platoon communication while maximizing V2I capacity using a distributionally robust optimization technique. Although this study considers both platoon and general vehicles, it assumes that general vehicles engage solely in V2I communication with the base station. Moreover, in typical resource allocation processes, vehicles provide the base station with information such as resource requirements, location, and service type, and the base station does not directly know the destination address of the packets being transmitted. Accordingly, this study introduced a dedicated communication to transmit the destination information of each vehicle to the base station, which consequently increased latency.