## 4 Evaluation
### CPU Time Comparison  
In this section, we present the CPU time taken to solve the considered approaches. Figure 11 shows the CPU time of the considered approaches for the simple scenario with varying number of VUs. Both SSF/opt and RND/opt exhibit the least time for solving both phase decisions due to their rule-based simplicity in the first phase. Since this phase employs straightforward algorithms, the computational time required is negligible. However, these methods still solve an optimization problem for power allocation during the second phase, similar to the other approaches. On the other hand, Proposed, R&R/opt, and Util&LB/opt tackle optimization problems in both phases, leading to higher CPU times compared to SSF/opt and RND/opt. When there is only a single vehicle user (VU) in the network, the CPU time differences between Proposed, R&R/opt, and Util&LB/opt are negligible. As the number  
Figure 11: Comparison of the time taken to solve the considered problems with varying numbers of vehicular users on a single-channel stationary configuration. Figure 10: Accumulated total power consumption for a long-term complex, mobile configuration with twenty VUs and three channels where each time step spans 100 ms.  
of VUs increases, the computational time for Proposed grows significantly compared to R&R/opt and Util&LB/opt. This difference arises primarily due to the complexity of the first phase. In Proposed, the first phase involves a centralized optimization problem with the largest number of binary variables, which adds to the computational overhead. R&R/opt simplifies this process by relaxing the binary variables, effectively reducing the time complexity. Meanwhile, Util&LB/opt reduces complexity by splitting the problem into smaller, more manageable parts: it first solves the association problem with fewer binary variables, followed by distributed, per-RSU optimization for channel assignment and NOMA-OMA switching. This partitioned approach substantially decreases CPU time. The mean (standard deviation) CPU time for Proposed, R&R/opt, Util&LB/opt, SSF/opt, and RND/opt is 0.0906 (0.0078), 0.0746 (0.0055), 0.0706 (0.0017), 0.0332 (0.0034), and 0.0318 (0.0029) seconds, respectively. As the network grows more complex, the differences in CPU time between these approaches become more pronounced. Figure 12 illustrates the mean CPU time required to solve the optimization problems in both stationary and mobile scenarios. The figure highlights that Proposed requires the most time due to its centralized and binary optimization framework for both phases. On the other hand, the rule-based approaches, SSF/opt and RND/opt, continue to show the least CPU time. Notably, the gap between R&R/opt and Util&LB/opt also widens in complex scenarios. Although Util&LB/opt partitions the problem to manage complexity, the presence of binary variables still causes a significant increase in computational time as the number of VUs and channels grows. Conversely, R&R/opt's relaxation of binary variables enables it to handle increased complexity more efficiently than Util&LB/opt. Our evaluation highlights the uniqueness of the proposed algorithm in its joint optimization of RSU-VU association, channel assignment, NOMA-OMA switching, and power allocation. Unlike other approaches, which handle these components separately, our method integrates them into a unified framework. This integration enables significant reductions in power consumption Figure 4, improved association fairness Figure 5, and increased utilization of NOMA channels Figure 6. Furthermore, the use of binary relaxation and distributed computation ensures computational feasibility for real-time applications, as demonstrated in CPU time comparisons Figures 11 and 12. These results confirm the effectiveness of our novel dynamic switching strategy and its adaptability to diverse vehicular network scenarios. Figure 12: Comparison of the time taken to solve the considered problems with twenty users on a three-channel mobile and stationary configuration.