# Reinforcement Learning Approach for Adaptive C-V2X Resource Management
## 1 Introduction
Nevertheless, it should be noted that the research assumes that the MCS configurations stay constant during the simulations. Additionally, it needs to analyze how the MCS adaptation operates regarding congestion management, specifically following the standards established by the ETSI [23]. These standards require that the MCS adaptation perform dynamically throughout continuous transmissions and in response to the congestion circumstances present in the channel. Additional scholarly works, including references [27; 28], explore the feasibility of using MCS adaptation as a potential resolution for transmitting vehicular packets of varying sizes via C-V2X technology. These studies provide a comprehensive examination of the consequences of MCS adaptation and underscore the significance of this method for the specific context in question. Nevertheless, the current models need to include the functioning of MCS adaptation, which is crucial for evaluating the effectiveness of this method in scenarios where packet sizes vary. The use of MCS adaptability in C-V2X may extend beyond the scope of existing research on the subject. Several studies show this phenomenon using the wireless 802.11p vehicular standard. As shown in references [29; 30], the use of MCS adaptation has proven effective in enhancing the efficiency of broadcasts via the dynamic adjustment of the MCS to accommodate changing channel circumstances. The authors in reference [31] emphasize the significance of MCS adaptability concerning the effectiveness of vehicle unicast transmissions, a characteristic of NR-V2X as specified in 5G Release 16 [2]. Despite the significance of evaluating the impacts of implementing the adaptive MCS in the vehicle cellular sidelink, this matter has yet to be explored in scholarly literature. This research aims to tackle this difficulty by thoroughly examining the impacts of the MCS adaptation on the functioning of the C-V2X sidelink. This study specifically examines the effects of MCS adaptation on the resource allocation of C-V2X, the calculation of power levels in the channel, and their implications for the functioning of SB-SPS. This study conducts a comprehensive analysis and precise modeling of the MCS adaptation's performance implications. To the authors' knowledge, this particular area of research has yet to be previously investigated. Ultimately, this research establishes the fundamental principles for future studies seeking to use MCS adaptability in many contexts. This work utilizes the overall PRR statistic over time as a reinforcement learning (RL) input. While the statistical PRR will be calculated on each packet transmission, we assume that the following network conditions, such as channel information and the signal-to-interference and noise ratio (SINR), are already calculated and carried out in the PRRrecords. The following new MCS level will be decided based on the subsequent PRR records. Since the overall PRR record represents the given network and environmental information, our approach will be able to satisfy the real-world scenario. In this work, we proposed the application of the RL algorithm, as we used the Q-learning approach to adaptively change the MCS index according to the current environmental states. The main contributions in this work can be described as follows:  
* We conducted several simulations and showed that static MCS index values had yet to achieve optimal performance. * We proposed a design of the Q-Learning RL approach to override the MCS index value dynamically. * Our RL algorithm approach was built on a MATLAB-based simulator and learned as the simulation ran within the specified time limit (T). * Performance measurement was carried out by comparing PRR results for each static MCS index value scenario with our proposed RL approach. The remainder of the paper can be explained as follows: Section 2 provides an explanation of the technology. Section 3 describes the proposed RL approach. Section 4 describes the simulation configuration and simulation results.