# Advanced Optimization Algorithm Combining a Fuzzy Inference System for Vehicular Communications
## 3 Proposed FIRL Resource Allocation  
This study examines the effectiveness of the FIRL method compared to a static baseline configuration for PRR in a vehicular network. This research utilizes the LTEV2 Vsim simulator, a MATLAB-based tool designed explicitly for C-V2X resource management investigations, to simulate the C-V2X network and communication systems [30]. MATLAB version R2021a is used for this work. The hardware used to execute the LTEV2 Vsim simulations is a ROG Strix G713 RM laptop with an AMD Ryzen 9 6900 HX 3.30 GHz, 16 GB of RAM, and 1 TB of solid-state disk (SSD) storage. The simulation scenario consists of a 1 km highway with bidirectional three-lane traffic, where vehicles travel at an average speed of 120 km/h with a standard deviation of 40 km/h. Key parameters of the simulation include a 120 s duration, position updates every 0.1 s, a channel bandwidth of 10 MHz, and a transmission power of 23 dBm. Table 2 briefly outlines the radio resource parameters employed in the simulation. Since the proposed FIRL aims to adjust the MCS and \(P_{rk}\) values adaptively, the range of \(I_{MCS}\) is set to [1, 2, 3,..., 28], and the range of possible \(P_{rk}\) values is set from 0 to 1. In this work, we present various total numbers of vehicles (\(n_{V}\)) from 100, 200, 300, and 400. The total number of vehicles cap presents the terms of vehicle density that could be loaded into the three-lane bi-directional highway with a 1 km length. The PRR is a critical metric for evaluating the statistical performance of communication systems, explicitly quantifying the proportion of successfully received packets relative to the total transmitted packets over distances ranging from 10 to 150 m. This study focuses on the radio resource management (RRM) aspect of C-V2X communication, a crucial component in optimizing PRR. RRM involves strategically allocating resource blocks to individual vehicles based on their MCS index, aiming to maximize PRR. The research presented herein aims to investigate the impact of RRM strategies on the efficacy of C-V2X communication, particularly in terms of successful packet transmission. By examining this relationship, this study seeks to contribute to developing and refining C-V2X technologies, potentially informing future advancements in vehicular communication systems. The overall \(PRR\) performance statistic can be obtained by  
\[PRR=\frac{g_{rcc}}{G_{tr}}, \tag{8}\] \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Simulation time \((T)\) & 120 s & Transmission power \((P_{Tx})\) & 23 dBm \\ Vehicle position update & 0.1 s & Beacon size & 190 Bytes \\ time \((t_{r})\) & & & \\ Vehicle’s speed & 120 km/h & Sensing interval & 0.1 s \\ Vehicle’s speed standard deviation & 40 km/h & MCS Index value & 1–28 \\ Road length & 1000 m & Probability resource keep \((P_{rk})\) & 0–1 \\ Number of lanes & 3 per directions & Q-learning predefined epsilon \((\varepsilon)\) & 0.1, 0.9 \\ Total number of vehicles \((n_{V})\) & 100–400 vehicles & Q-learning discount factor \((\gamma)\) & 0.1 \\ Channel bandwidth \((B_{c})\) & 10 Mhz & Q-learning learning rate \((\alpha)\) & 0.1, 0.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters and settings. where \(\varsigma_{rcv}\) is the total number of successfully received packets and \(\varsigma_{tr}\) is the total number of transmitted packets. The PRR is a statistical metric that quantifies the effectiveness of packet transmission within a network by calculating the ratio of successfully received packets to the total number of packets transmitted, including retransmissions. To simulate real-world scenarios, transmitting and receiving vehicles are assigned randomly. The PRR is a crucial parameter for assessing wireless network performance. During a designated simulation period (\(T\)), data were collected to determine the average number of packets transmitted and received, which aids in identifying potential issues and enhancing the overall performance of the network.