# Advanced Optimization Algorithm Combining a Fuzzy Inference System for Vehicular Communications
## 2 System Models
### C-V2X Resource Management  
The MCS plays a pivotal role in modern communication systems by directly influencing data transmission efficiency. It defines the modulation order, which reflects the level of modulation applied to the communication channel and is a key determinant of system performance. Standard modulation orders are represented by schemes such as QPSK, 16 QAM, 64 QAM, and 256 QAM, with the parameter \(Q_{m}\) in 3 GPP standards specifically used to denote this measure. The choice of modulation order significantly impacts the overall performance and reliability of the communication system [22]. The MCS index (\(I_{MCS}\)) is a crucial parameter in C-V2X communication that determines the maximum number of valuable bits transmitted per resource element (RE). This index is influenced by various factors, including modulation order, channel bandwidth, and signal-to-noise ratio, with higher values enabling more efficient data transmission. The performance of C-V2X communication is significantly impacted by the quality of the radio connection, which can be affected by signal interference, fading, and transmitter-receiver distance. A superior radio connection quality enhances transmission capacity, vital for real-time critical data transmission. To optimize C-V2X communication performance, it is essential to balance the MCS index and radio connection quality, carefully selecting the appropriate modulation order and MCS index for specific communication scenarios. This approach ensures efficient and reliable data transmission in vehicular networks, improving road safety and traffic management [22]. The quality of radio conditions is intrinsically linked to the \(I_{MCS}\) employed, with deteriorating conditions necessitating a downgrade in \(I_{MCS}\), thereby significantly reducing the capacity for essential data transmission. The performance of the MCS is contingent upon the frequency of error occurrences, with a typical error probability threshold set at 10%. To maintain the error probability below this threshold, even in fluctuating radio conditions, the \(I_{MCS}\) is dynamically adjusted. Subsequently, the \(I_{MCS}\) is utilized to estimate the transport block size (TBS). The 3 GPP has standardized the \(I_{MCS}\), TBS index (\(I_{TBS}\)), and associated modulation order (\(Q_{m}\)) values, ensuring that the \(I_{MCS}\) is optimized to provide the highest possible data transmission rate while maintaining a low error rate, even under challenging radio conditions [20]. The determination of the requisite number of physical resource blocks for beacon data transmission, measured in bits, necessitates the calculation of \(I_{TBS}\) based on \(I_{MCS}\), utilizing a reference table provided by 3 GPP that delineates the TBS indices and corresponding physical resource block quantities regarding beacon data sizes. The interplay between the TBS index, physical resource block count, and beacon data size is paramount in optimizing resource allocation for beacon data transmission. In the context of Mode 4 configuration, the beacon data transmission process comprises two crucial elements, the beacon data transport block (TB) and the SCI, each requiring discrete resource allocation to ensure efficient and reliable transmission of the beacon payload. This approach enhancesthe clarity and precision of resource allocation strategies in wireless communication systems, particularly in vehicular networks and other applications utilizing beacon-based communication protocols. Contemporary communication systems operate at signal-to-interference-plus-noise ratios (SINRs) that approach the maximum threshold, with only marginal differences in a few decibels. However, various modulation and radio schemes exhibit differing proximities to this limit, necessitating the introduction of two parameters to differentiate their performance. A key metric derived from the capacity formula, Modulation Efficiency or Channel Spectral Efficiency, is employed to evaluate these modulation schemes, quantifying their efficiency in data transmission.