## 5 Simulation Results and Analysis
### Simulation Result
Figure 3: Average AoI between LTE-V2X and NR-V2X. the agent has learned a strategy close to optimal. There is some jitter in the curves around episode 1000 when the number of vehicles is 40 and 50. This is attributed to detection noise impacting the agent, necessitating adjustments to return to a convergent state. Furthermore, it is noticeable that as the number of vehicles increases, the rewards decrease. This is due to the increasing interference experienced by each device with the growing number of devices in the system, resulting in lower SINR. This leads to prolonged transmission delays and increases the system AoI. To maintain a lower AoI, RSUs notify more vehicles to utilize communication resources for transmission, that is, more vehicles imply a higher AoI and increased energy consumption, and hence, lower rewards. Figure 7 depicts the variation in average AoI with the numbers of vehicles in the NOMA-based NR-V2X vehicular network system when employing MPDQN, genetic algorithm, and random algorithm strategies. It can be observed that the AoI of all three strategies increases with the increase in the number of devices. This is attributed to the interference experienced by each device as the number of devices increases, leading to increased transmission times according to the equation, which may further increase the AoI of systems. Furthermore, the allocation strategies obtained by MPDQN, which approximates the optimal strategy, and the genetic algorithm consistently outperform the random strategy. This is because the near-optimal strategy obtained by MPDQN selects actions for vehicles based on observed states, while the genetic algorithm derives better action allocation strategies through evolution, whereas the random strategy merely generates action allocations randomly. Additionally, it can be observed that the strategy derived from MPDQN outperforms that of the genetic algorithm, resulting in lower average AoI for vehicles using MPDQN. This is because MPDQN considers the impact of action allocations for each time slot on subsequent AoI, whereas the genetic algorithm does not. Figure 6: Learning curves under different numbers of vehicles. Figure 7: Average AoI with \(N_{b}\) under different algorithms. Figure 8 compares the energy consumption within the system when vehicles employ three different methods. It can be observed that energy consumption increases when the number of devices increases. Moreover, the energy consumption in the random method does not exhibit significant variations for different numbers of vehicles within the system, resulting in a more linear energy consumption pattern. On the other hand, in MPDQN and genetic algorithm methods, the increase increase in the number of vehicles leads to an increase in interference power, resulting in a decrease in SINR. The reduced SINR leads to a longer transmission time and a higher probability of exceeding transmission time slots, thereby increasing the average information age of the system. However, due to the relatively larger weight of average energy consumption in the optimization objective, RSUs may choose to incur minimal additional energy consumption when the information age is low. Thus, the impact of the number of vehicles on energy consumption is less pronounced compared to its impact on information age. Furthermore, the strategies obtained by MPDQN and the genetic algorithm consistently outperform the random strategy, as they can derive better actions to ensure lower energy consumption costs at low AoI through their respective optimization approaches. Additionally, it can be observed that MPDQN consistently outperforms the genetic algorithm when the number of vehicles is high. This is because MPDQN has an advantage in handling real-time decision-making and dynamic environments, allowing it to continuously adjust strategies based on environmental feedback, resulting in stronger adaptability. In scenarios with a higher number of vehicles, MPDQN may have an easier time learning optimal scheduling strategies through interaction with the environment.