## 2 Overview of 5G-NR-V2X Mode 2
DS in vehicular networks allows vehicles to randomly select subchannels for each transmitted message without prior reservations, assuming that all subchannels within the selection window are available, as shown in Table 1. This method, like a multichannel slotted Aloha strategy, operates independently of the selection window's width and PDB. The collision probability under DS is primarily influenced by the message generation rate and size, making it an efficient approach that minimizes collision risks, particularly when only a subset of vehicles adopts this scheme. DS on the other hand, involves the random selection of new subchannels for each transmission without pre-reserving resources. This scheme better handles aperiodic and variable traffic but faces the following significant challenges:  
* High collision probability: Random selection increases collision risk, especially in dense traffic scenarios;
* Lack of coordination: Real-time resource selection can lead to inefficient subchannel usage and higher interference;
* PDB issues: DS does not explicitly consider the PDB for individual packets, potentially missing strict latency requirements for delay-sensitive applications, leading to delays and suboptimal performance. The current landscape of research on resource allocation strategies in 5G-NR-V2X Mode 2, as summarized in the comparison in Table 2, highlights significant efforts to address the limitations of the SB-SPS and DS techniques, particularly in handling aperiodic traffic and periodic traffic. In Table 2, a cross (\(X\)) indicates that the technique is not included in the respective study, while a checkmark (\(\checkmark\)) signifies that it is included. Various approaches, including Multi-Agent Actor-Critic (IAC, SEAC) [2], deep reinforcement learning (Q-learning) [5; 9; 10], and SB-SPS with lookahead mechanisms [11; 12; 13], have been explored to improve reliability, reduce collisions, and enhance resource utilization in V2X communications. However, despite these advances, no existing work has fully addressed the challenges associated with SB-SPS in aperiodic traffic, nor has there been substantial exploration of DS, especially under conditions of high traffic density where the likelihood of collisions increases due to the random resource selection inherent in DS [14; 15]. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Counter Reselection** & **Size Reselection** & **Latency Reselection** \\ \hline SB-SPS & \(C_{resel}=0\), depends on \(P\) & TB size larger than reservation size & RRI \textgreater{} PDB \\ DS & Every TB & Never & Never \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Reselection types in 5G-NR-V2X Mode 2. Figure 2: Impact of varying traffic types in 5G-NR-V2X Mode 2. This work addresses these gaps by proposing a SEAC reinforcement learning technique that not only overcomes the limitations of SB-SPS but also enhances the reliability and resource utilization in aperiodic environments. As demonstrated by the results, SEAC significantly outperforms both SB-SPS and DS, particularly in scenarios where the traffic density is high, proving to be a superior method for reducing collisions and optimizing resource allocation.