## 5 Conclusions  
In this study, we have explored an optimized approach for scheduling a series of essential operations for effectively applying power domain NOMA on vehicular networks. Compared to the previous studies that focus on one or a few aspects, we propose optimizing roadside unit-vehicle user association, channel assignment and power control to fully leverage the advantages of NOMA. The proposed optimization formulation has demonstrated outstanding performance through extensive evaluation. Additionally, to reduce the time complexity of the optimization formulation with complex network configurations, we propose two alternative formulations that can operate efficiently with minimal performance degradation. The inclusion of additional metrics such as SINR and throughput demonstrates the robustness and real-time applicability of the proposed algorithm. These results validate the algorithm's ability to minimize power consumption while maintaining high communication quality and meeting the constraints, crucial for urban vehicular network scenarios. Our proposed approach is expected to expedite the use of NOMA technology in mobile and dense populated urban vehicular networks, significantly enhancing user experience and safety to a larger extent.  
Future Research DirectionsThe promising future research directions include incorporating dynamic traffic models on rural and urban scenarios, integrating deep learning models for improved adaptability with high generalization performance, extending to heterogeneous networks such as Vehicular Internet of Things (VIoT) [37], and validating on real-world testbeds. These steps will further advance NOMA's application in vehicular networks.  
Conceptualization, A.B. and T.K.; methodology, T.K., Y.K. ([PERSON]) and Y.K. ([PERSON]); software, A.B. and T.K.; validation, S.S., Y.K. ([PERSON]), Y.K. ([PERSON]) and T.K.; formal analysis, S.S. and T.K.; investigation, A.B. and T.K.; resources, T.K.; data curation, A.B.; writing--original draft preparation, A.B. and T.K.; writing--review and editing, S.S., Y.K. ([PERSON]) and Y.K. ([PERSON]); visualization, A.B. and T.K.; supervision, T.K.; project administration, T.K.; funding acquisition, T.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2022R1A4A1033600) and in part by Institute of Information & communications Technology Planning & Evaluation (IITP) under the Artificial Intelligence Convergence Innovation Human Resources Development (IITP-2024-RS-2023-00254177) grant funded by the Korea government (MSIT).  
Not applicable.  
Data Availability StatementData are contained within the article.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** &
\begin{tabular}{c} **Power Consumption** \\ **(dBm)** \\ \end{tabular} & **Fairness (Jain’s Index)** & **CPU Time (s)** \\ \hline Proposed & 27.0978 & 0.8567 & 0.1301 \\ R\&R/opt & 34.9394 & 0.9685 & 0.0911 \\ Util\&LB/opt & 35.2216 & 0.9963 & 0.1158 \\ SSF/opt & 37.4326 & 0.8650 & 0.0408 \\ RND/opt & 38.6501 & 0.8840 & 0.0389 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Overall performance comparison for a complex configuration with twenty VUs and three channels with and without mobility.