# Advanced Optimization Algorithm Combining a Fuzzy Inference System for Vehicular Communications
## 3 Proposed FIRL Resource Allocation
Previous studies, as noted in references [31; 32], highlight that an MCS index of seven and a \(P_{rk}\) value of 0.8 are commonly adopted configurations among researchers. Consequently, this work establishes the benchmark baseline configuration using MCS = 7 and \(P_{rk}\) = 0.8 as static parameters for subsequent comparisons. This paper conducts a comprehensive investigation into the performance of the FIRL sequence approach, with a specific emphasis on evaluating the PRR under the sequential implementation of Sugeno-FIS and Q-learning methodologies. The objective is to develop fully adaptive configuration parameters for \(P_{rk}\) and MCS, ultimately enhancing overall network performance. This study implemented a sequencing mechanism to achieve the research objectives, as illustrated in the flowchart presented in Figure 6. To elucidate the sequential steps of this mechanism, Algorithm 1 was developed, wherein the resource keep probability (\(P_{rk}\)) and MCS values adaptively adjust with each generated Long-Term Evolution (LTE) packet. This approach is expected to contribute significantly to developing adaptive PRR configurations in wireless communication systems. Moreover, this research provides valuable insights into the optimal implementation of sequence mechanisms in wireless communication systems that leverage machine learning algorithms. These findings could enhance the efficiency and reliability of wireless networks, particularly in dynamic environments where adaptive strategies are crucial for maintaining optimal performance. The sequence execution process employs a straightforward approach in which the \(P_{rk}\) and MCS adaptation procedures are performed concurrently with LTE packet generation. The Sugeno-FIS routine followed by the Q-learning routine is executed within the same time frame. The sequence routine yields real-time MCS index and \(P_{rk}\) parameter values, subsequently determining the adapted values for MCS and \(P_{rk}\). Consequently, the PRR is adjusted based on these adapted values, reflecting the dynamic nature of the network's performance optimization process. This integrated approach allows for real-time adaptation of transmission parameters, potentially enhancing the overall efficiency and reliability of the LTE communication system. ```
1:Initialize:
2:input input : \(T\), \(P_{rk}\), \(I_{MCS}\), \(\alpha\), \(\gamma\), \(\varepsilon\), \(PRR_{0}\)
3:while\(t\leq T\)do
4:for each LTE packet generation
5:execute Q-learning subprocess; output: \(I_{MCS_{t}}\)
6: execute Sugeno-FIS subprocess; output: \(P_{rk_{t}}\)
7: calculate real-time PRR; output: \(PRR_{t}\)
8:endwhile
```  
**Algorithm 1** Pseudocode of FIRL sequence The execution of the FIRL sequence is a crucial component in the generation of LTE packets during vehicular data transmission. This process is initiated when the FIS and  
Figure 6: FIRL flowchart. Q-learning adaptation approaches activate the LTE packet generator. During the FIRL function's execution, the current output parameters for the \(P_{rk}\) and the MCS index are dynamically adjusted based on the prevailing network environmental conditions. These updated \(P_{rk}\) and MCS index values are subsequently incorporated into the PRR calculation, serving as key inputs for assessing network performance. Figure 6 presents a comprehensive flowchart depicting a simulation process for LTE packet transmission incorporating reinforcement learning and fuzzy inference systems, named FIRL. The workflow begins with simulation initialization and initial simulation parameters, followed by the first radio resources assignment, and the cycle starts at \(t=0\).