# Advanced Optimization Algorithm Combining a Fuzzy Inference System for Vehicular Communications
This technology's capacity to select optimal modulation and coding schemes in response to changing environments promises to significantly improve connected vehicles' safety, comfort, and efficiency. MCS adaptation has demonstrated effectiveness in enhancing broadcast transmission efficiency through dynamic adjustments to channel conditions. Furthermore, researchers have emphasized the crucial role of MCS adaptability in vehicle unicast transmissions, a key feature of New Radio V2X (NR-V2X), as defined in 5G Release 16 [2]. These advancements in MCS adaptability for C-V2X systems represent a promising direction for future research and development in vehicular communication technologies [12]. Q-learning, while a powerful reinforcement learning technique, faces several limitations in C-V2X scenarios. One significant challenge is the curse of dimensionality, which arises when jointly considering multi-dimensional resources in complex vehicular networks [13]. These multi-dimensional resources can lead to an explosion in the stateaction space, making it computationally intractable to explore all possible combinations. Additionally, Q-learning struggles with stability and convergence issues when using non-linearapproximations like deep neural networks, potentially resulting in divergence or oscillations [14]. The algorithm also tends to produce over-optimistic value estimations because the max operator selects and evaluates actions using the same values [15]. Despite these limitations, Q-learning offers several advantages in C-V2X applications. It can effectively learn optimal policies in dynamic environments without requiring a complete model of the system [13; 14]. Q-learning's ability to balance exploration and exploitation through techniques like \(\varepsilon\)-greedy allows for it to adapt to changing network conditions [15]. Furthermore, when combined with deep learning techniques, such as in Deep Q-Networks (DQN), it can automatically extract relevant features and handle high-dimensional state spaces [16]. Experience replay, and target networks in DQN variants help stabilize learning and improve sample efficiency [15]. These advantages make Q-learning a promising approach for tackling complex resource allocation and decision-making problems in C-V2X scenarios, mainly when dealing with partially observable or unknown system dynamics [17]. The application of Fuzzy technology in dynamic resource management, specifically through Fuzzy Matching Learning, has been reported to enhance the efficiency and effectiveness of resource allocation by considering a more comprehensive range of factors and variables relevant to a given context [18]. This technique enables identifying and allocating resources based on non-binary or non-deterministic rules and criteria. Furthermore, an integrated resource management mechanism has been implemented in a C-V2X environment, prioritizing the Quality-of-Forwarding (QoF) mechanism [19]. This approach aims to ensure high-quality and reliable data transmission between vehicles, thereby enhancing the performance of C-V2X systems and enabling safer and more efficient vehicular communication. Integrating these advanced technologies in resource management and vehicular communication systems represents a significant step towards optimizing resource allocation and improving the efficiency of intelligent transportation systems. This study investigates the impact of MCS adaptation on resource allocation in C-V2X networks, with a particular focus on maintaining the efficacy of SB-SPS. The researchers conduct a comprehensive analysis and precise modeling of the performance implications of MCS adaptation and \(P_{rk}\) value adjustments. The authors propose a novel approach integrating reinforcement learning, specifically the Q-learning algorithm, with the Sugeno Fuzzy Inference System (FIS) to optimize the MCS index based on current environmental conditions. This combination enhances the accuracy and adaptability of the reinforcement learning algorithm.