## 4 Simulation Environments
### Performance Metrics  
In this study, the performance of the SEAC reinforcement learning technique is compared with traditional SB-SPS and DS schemes using the following metrics:f  
* Packet reception ratio (PRR): The PRR measures the percentage of correctly received and decoded transmission bursts (TBs) compared to the total transmitted TBs. It is evaluated as a function of the distance (D) between transmitting and receiving vehicles, providing insight into the reliability of communications over varying distances. \[PRR\;(D)=\frac{N_{\text{rec}}\;(D)}{N_{\text{trans}}(D)}\times 100\] (18) where  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Message Type** & **ETSI Standard** & **Scope** & **Traffic Characteristic** \\ \hline \multirow{4}{*}{CAM—Cooperative Awareness Message} & \multirow{4}{*}{TS 103900} & CAMs periodically & \multirow{4}{*}{1–10 Hz, \(\sim\)400 bytes} \\  & & broadcast vehicle & \\ \cline{1-2} \cline{4-4}  & & status, like position & \\ \cline{1-2} \cline{4-4}  & & and speed, to improve & \\ \cline{1-2} \cline{4-4}  & & situational awareness. & \\ \cline{1-2} \cline{4-4}  & & CPMs share sensor data & \\ \cline{1-2} \cline{4-4}  & & about detected objects, & \\ \cline{1-2} \cline{4-4}  & & aiding in collective & \\ \cline{1-2} \cline{4-4}  & & traffic perception & \\ \cline{1-2} \cline{4-4}  & & and management. & \\ \hline \hline \end{tabular}
\end{table}
Table 4: CAMs and CPMs.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Values** \\ \hline Network & 5G-NR-V2X Mode 2 \\ Highway Segment Length & 2 km \\ Lanes Per Direction & 3 \\ Initial RSRP Threshold & \(-\)128 dBm \\ Mean Vehicle Speed & 140 km/h \\ Standard Deviation of Speed & 3 km/h \\ Reselection Counter & [5; 15] \\ Data Traffic Type & CAM and CPM broadcast \\ Subcarrier Spacing (SCS) & 15 kHz \\ Operational Mode & Unmanaged \\ Channel Bandwidth & 20 MHz \\ Operating Band & 5.9 GHz ITS band \\ Modulation Scheme & QPSK \\ Transmission Power & 0–23 dBm \\ Receiver Sensitivity & \(-\)103.5 dBm \\ Resource Reservation Interval & 100 ms \\ Proximity Broadcast Range & 500 m \\ Traffic Type & Aperiodic \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.  
\(N_{rec}\)\((D)\) is the number of TBs correctly received and decoded at distance D;
2. \(N_{trans}(D)\) is the total number of TBs transmitted at distance D.
* Radio resource utilization (RRU): This metric evaluates the efficiency with which the communication system uses the available radio spectrum. It reflects the effectiveness of resource management in maintaining high network performance and minimizing wasted resources. \[RRU=\frac{N_{used}}{N_{available}}\times 100\] (19) where 1. \(N_{used}\) is the number of radio resources that were successfully used for communicating;
2. \(N_{available}\) is the total number of radio resources available for use.
* Probability of resource collision (PRC): This metric assesses the likelihood of multiple vehicles selecting the same radio resources simultaneously, causing communication collisions. A high PRC indicates frequent resource contention, leading to degraded network performance. Minimizing the PRC is essential for efficient spectrum utilization and reliable communication, especially in dense traffic scenarios. \[RRU=\frac{N_{collisions}}{N_{total\_ attempts}}\times 100\] (20) where 1. \(N_{collisions}\) is the number of instances where multiple vehicles selected the same resource, causing a collision;
2. \(N_{total\_ attempts}\) is the total number of attempts made by vehicles to access the resources.  
In addition to these metrics, the effectiveness of the SEAC technique is further demonstrated through training graphs, illustrating the model's learning progress. These graphs provide a visual representation of how well the SEAC model improves its resource allocation decisions over time, showcasing its potential advantages over traditional methods.