## 4 Evaluation
### Results of Simple and Stationary Scenarios  
We first carry out an evaluation of a simple network configuration to assess the performance in depth. On the assumed scenarios, there can be one to ten VUs on the road,  
Figure 3: An example urban intersection road scenario with five RSUs and twenty vehicle users denoted by black circles and red triangles, respectively. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Area & 300 m-by-300 m \\ Central frequency & 850 MHz \\ Channel bandwidth & 180 kHz \\ Mobility & Random waypoint, [8.33,16.67] m/s. \\ Number of RSUs & 5 \\ Number of VUs & 1–10 and 20 \\ Number of orthogonal channels & 1 and 3 \\ Power budget & 46 dBm \\ QoS & Uniform [0.6, 2.0] Mbps \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Fundamental experiment setup. and each RSU can utilize only a single channel for communication. As a result, when there are only a few VUs on the network, most RSUs can activate OMA communications, but as the number of VUs increases, RSUs have no choice but to switch to NOMA to accommodate more VUs. As shown in Figure 4, the proposed approach achieves the lowest power consumption due to its ability to jointly optimize resource allocation across multiple layers, a feature absent in the other methods. It also shows the mean total power consumption with respect to the number of VUs. It is obvious that as the number of VUs increases the network consumes more power to transmit a larger amount of traffic to more users. However, Proposed successfully minimizes the total power consumption. It is worth noting that all the considered approaches satisfy the QoS requirement since it is coded as a constraint. On the other hand, RND/opt uses the largest amount of power in most cases for its randomness in association, channel assignment and NOMA-OMA switching decisions. SSF/opt can reduce the power consumption when there is a lower number of users, but when the number of users exceeds seven, it is outperformed by RND/opt. The reason is that although RND/opt has no strategy in making decisions, its uniform randomness can exercise a certain level of load distribution. However, SSF/opt greedily allocates users only by considering the channel condition, and thus, when NOMA is triggered, power has been allocated inefficiently. To evaluate our proposed method, we compared it with four alternatives: R&R/opt, Util&LB/opt, SSF/opt, and RND/opt as mentioned in Section 4. The proposed method delivered the best performance, reducing power consumption by up to 27%. Its joint optimization of RSU-VU association, channel assignment, NOMA-OMA switching, and power control ensures high efficiency in dense urban networks, but increases computational complexity and slightly impacts fairness. All the considered methods were tested under identical simulation conditions and evaluated based on the following four metrics: power consumption, association fairness (Jain's index), NOMA channel usage, and CPU time. The scenarios included both low-density and high-density urban vehicular networks with/without mobility to fairly assess each method's strengths and limitations. The reported values are the average over 10 simulations, and we have considered only the scenarios where all the considered approaches should satisfy the desired QoS requirement for a fair comparison. Due to the inherent similarity between Util&LB/opt and SSF/opt, their power usage patterns tend to be similar to each other, although Util&LB/opt outperforms SSF/opt for distributing the VUs across RSUs. Although R&R/opt is outperfo  
Figure 4: Mean total power consumption for a simple and stationary configuration with a single channel with respect to the number of VUs on the network. in some configurations, its overall power consumption is lesser than others except Proposed. The reason for that is it jointly optimizes association, channel assignment and NOMA-OMA switching as Proposed does.