## 5 Simulation Results and Analysis
### Simulation Result  
In this section, we first compare the AoI of vehicles in LTE-V2X and NR-V2X. Then, we compare the AoI of vehicles in NR-V2X before and after using NOMA, as well as the AoI in LTE-V2X and NR-V2X based on NOMA. Finally, we optimize the joint objective of AoI and energy consumption in NR-V2X using MPDQN. Many recent works have employed genetic algorithms [36] and random algorithms [37] as baseline algorithms for resource allocation, and thus, we shall compare our approach with these two methods above. Figure 3 illustrates the variation in average AoI in the system as the number of vehicles using LTE-V2X and NR-V2X for V2V direct communication scenarios. The number of vehicles considered are 20, 30, 40, and 50, with each vehicle following 3 GPP standards and employing a random strategy to select its RRI (Resource Reuse Interval) and transmission power. It is observed that the average AoI in the system increases with the number of vehicles, regardless of whether the LTE-V2X or NR-V2X communication mode is utilized. This AoI increase can be attributed to the expansion of the receiver set within the communication range of vehicles as the number of vehicles in the system increases, leading to increased interference among them. Furthermore, due to the half-duplex communication mode, more vehicles are unable to receive messages due to resource contention, resulting in an increase in the average AoI. Additionally, as NR-V2X serves as a complement and advancement to LTE-V2X, the average AoI of vehicles in a vehicular networking system using NR-V2X for communication operations is consistently lower than that in an LTE-V2X system. Figure 4 illustrates the variation in average AoI in the system as the number of vehicles changes when vehicles use NR-V2X and V2V communication with NOMA enabled. In this scenario, vehicles randomly select their RRI (Resource Reuse Interval) and transmission power. It can be observed that when vehicles use NOMA, they exhibit lower AoI when the number of vehicles changes. Moreover, the overall growth trend is smoother. This is attributed to NOMA's power-domain-based decoding approach, which significantly mitigates the impact of different vehicles occupying the same resources. Similarly, the average AoI within the system increases with the number of vehicles due to the proliferation of receivers. When more receivers cannot successfully receive messages due to resource contention, the AoI increases as the number of vehicles become larger. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(N_{v}\) & 20, 30, 40, 50 & \(L\) & 10 \\ \hline \(D\) & 500 m & \(D_{RSU}\) & 250 m \\ \hline \(w\) & 150 m & \(P_{max}\) & 23 dBm \\ \hline \(v_{min}\) & 60 km/h & \(v_{max}\) & 80 km/h \\ \hline \(W\) & 10 MHz & \(RSRP_{th}\) & \(-\)126 dB \\ \hline \(lr_{Q}\) & 5 \(\times~{}10^{-4}\) & \(lr_{x}\) & \(10^{-4}\) \\ \hline \(\tau\) & 0.01 & \(\gamma\) & 0.99 \\ \hline \(M\) & 2000 & \(B\) & 128 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Values of the parameters in the experiments. Figure 5 depicts the variation in average AoI within the system as the number of vehicles changes when vehicles utilize NOMA-based NR-V2X and LTE-V2X for V2V communication. With NOMA incorporated, the relationship between the two remains consistent with Figure 3, where the average AoI of NR-V2X consistently remains lower than that of LTE-V2X scenarios. This persistent advantage of NR-V2X over LTE-V2X can be attributed to the fact that vehicles in NR-V2X experience fewer resource collisions even before the implementation of NOMA. Figure 6 illustrates the learning curves of training under different scenarios. Overall, it can be observed that the rewards of different curves exhibit an upward trend in fluctuation from episode 0 to episode 500, Subsequently, the learning curves stabilize, indicating that  
Figure 4: Average AoI before and after using NOMA for NR-V2X. Figure 5: Average AoI between LTE-V2X and NR-V2X based on NOMA.