# Advanced Optimization Algorithm Combining a Fuzzy Inference System for Vehicular Communications  
[PERSON]  
1 Faculty of Information Technology, Satya Wacana Christian University, Salatiga 50711, Indonesia;  
1 [EMAIL_ADDRESS] (T.H.B. ); [EMAIL_ADDRESS] (B.K. ); [EMAIL_ADDRESS] (E.C. );  
1 [EMAIL_ADDRESS] (S.S)  
[PERSON]  
2 Department of Information and Communication Engineering, Chaoyang University of Technology,  
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[PERSON]  
3 Department of Information Management, Chaoyang University of Technology, Taichung 413310, Taiwan2  
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[PERSON]  
4 Department of Computer Science and Information Engineering, Chaoyang University of Technology,  
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[PERSON]  
1 Faculty of Information Technology, Satya Wacana Christian University, Salatiga 50711, Indonesia;  
1 [EMAIL_ADDRESS] (T.H.B. ); [EMAIL_ADDRESS] (B.K. ); [EMAIL_ADDRESS] (E.C. );  
1 [EMAIL_ADDRESS] (S.S)  
[PERSON]  
1 Faculty of Information Technology, Satya Wacana Christian University, Salatiga 50711, Indonesia;  
1 [EMAIL_ADDRESS] (T.H.B. ); [EMAIL_ADDRESS] (B.K. ); [EMAIL_ADDRESS] (E.C. );  
1 [EMAIL_ADDRESS]  
factors such as cellular network availability, network reliability, device costs, and specific communication requirements [2]. Distributed congestion control plays a vital role in C-V2X communication, with real-time implementation recognized as a practical approach. In particular, the real-time adaptation of the MCS has been identified as a key mechanism to facilitate congestion control. This method has been supported by both 3 GPP standards and various academic studies as a viable solution, particularly for managing the transmission of packets with varying sizes [3]. While alternative methods may offer greater efficiency in certain contexts, real-time MCS adaptation remains valuable in specific scenarios [4; 5]. The studies conducted by the authors of references [6; 7] have explored the potential of MCS adaptation as a congestion management tool in C-V2X Mode 4. Their research suggests that real-time MCS adaptation could contribute to the distribution of congestion control and facilitate the transmission of diverse packet sizes. However, these authors emphasize the necessity of comprehensively examining the potential ramifications of integrating this mechanism into C-V2X SB-SPS operations to safeguard against any adverse effects on overall system performance. While they posit that elevating the MCS may alleviate congestion, they also caution that such an approach could lead to performance degradation. Consequently, the researchers underscore the critical importance of a thorough evaluation of all viable solutions prior to implementation to ensure optimal outcomes in C-V2X Mode 4 congestion management. The study presented by [8] offers a comprehensive examination of congestion management techniques in C-V2X communication, focusing on the impacts of various MCS configurations on C-V2X performance under vehicular congestion scenarios. However, it is crucial to note that this research employs static MCS configurations throughout the simulations, lacking an in-depth analysis of dynamic MCS adaptation for congestion management. This limitation is particularly significant when considering the standards set forth by the European Telecommunications Standards Institute (ETSI), which mandate dynamic MCS adaptation during continuous transmissions based on real-time channel congestion conditions [9]. The absence of this dynamic adaptation in the study highlights a potential area for further research to align more closely with practical implementation requirements and industry standards. The application of MCS adaptability in C-V2X communication has emerged as a significant area of research, extending beyond the scope of the widely used 802.11p vehicular standard [10]. Recent studies indicate that MCS adaptability in C-V2X has the potential to enhance the reliability and efficiency of vehicle-to-infrastructure communication by dynamically adjusting to varying network conditions [11].