## 4 Evaluation
### Results of Complex Stationary and Mobile Scenarios  
In this configuration, we have increased the complexity of the vehicular network scenario by increasing the number of VUs and channels to twenty and three, respectively. The effect of scenario complexity on the CPU processing time will be discussed shortly in Section 4.5. Figure 7 shows the mean total power consumption of the considered approaches. Proposed still outperforms the rest, validating its robustness regardless of the complexity of the scenario. Although the simple rounding technique degrades the solution quality, R&R/opt shows an outstanding performance, showing its effectiveness in a complex configuration as well. As the number of VUs and available orthogonal channels increases, fair distribution of VUs is an effective solution in genera and thus, the performance of Util&LB/opt is similar to R&R/opt. In contrast, the effectiveness of SSF/opt degrades as the scenario becomes complex since it only focuses on maximizing the total signal strength. Thus, when VUs are densely deployed around a small number of RSUs, most SSF/opt VUs associate with those RSUs, resulting in an inefficient power allocation when NOMA is exercised. Figure 8 shows the association fairness on a complex road scene. It is obvious that Util&LB/opt achieves the largest fairness as it should be. R&R/opt also has yielded a high fairness score because its simple rounding with random tie-breaking has resulted in a fair distribution of VUs. RND/opt randomly distributes the VUs and thus, it is able to achieve a certain level of fairness and thus, it outperforms Proposed and SSF/opt in terms of fairness score. SSF/opt only highlights maximizing the total signal strength sum, and thus its fairness score is low. Surprisingly, Proposed has recorded the lowest fairness score although the difference to SSF/opt is negligible. Cons  
Figure 6: Mean proportion of the NOMA channels for a simple, stationary configuration with a single channel with respect to the number of VUs on the network. consumed the least amount of power, such unfairness indicates that Proposed decides that a certain way of rather unfair association can yield the optimal performance in terms of power consumption. The following Figure 9 shows the proportion of the NOMA channel. Except RND/opt which randomly enables NOMA channels, Proposed, Util&LB/opt and SSF/opt utilize more NOMA channels compared to R&R/opt, although the difference is not significant. It is straightforward that if the fairness score is low, more NOMA channels should be utilized which is why Proposed and SSF/opt result in a high proportion of NOMA channels. On the other hand, R&R/opt's association fairness score is high and thus, the proportion of NOMA channel is relatively small. In the case of Util&LB/opt, although its association fairness score is high, Util&LB/opt utilizes the NOMA channel relatively much. After an in-depth analysis, we found that the randomness in channel conditions occasionally deters VUs from utilizing some channels. As a result, Util&LB/opt actively utilizes NOMA in some scenarios. Figure 8: Mean association fairness performance for a complex, stationary configuration with twenty VUs and three channels. Figure 7: Mean total power consumption for a complex, stationary configuration with twenty VUs and three channels. Finally, we conducted a long-term simulation to evaluate the performance of the proposed framework under vehicular mobility. The simulation was run for 50 timesteps, with each timestep spanning 100 ms. Vehicle users (VUs) were assumed to move toward the center of the intersection, with their speeds randomly selected to reflect realistic mobility patterns. To simplify the interpretation of results, the accumulated transmit power expenditure, measured in watts, is shown in Figure 10. As anticipated, Proposed demonstrates superior performance, consistently outperforming all other approaches. R&R/opt ranks second, benefiting from its optimization of the association, channel assignment, and NOMA-OMA switching components.