# Reinforcement Learning Approach for Adaptive C-V2X Resource Management
## 1 Introduction  
The introduction of vehicular communication technology is poised to significantly transform the transportation and mobility industry, enabling enhanced safety measures and increased efficiency in traffic management. Moreover, the advancement of vehicle autonomy, facilitated by improved sensing capabilities, is anticipated to bring out novel advanced services such as cooperative sensing and maneuvers. These services will heavily rely on direct sidelink connection. Vehicle communications criteria have primarily relied on IEEE 802.11p technology in the last ten years. Recently, the 3 GPP has expeditiously formulated cellular standards to tackle the obstacles associated with vehicular communications, collectively called C-V2X [1]. This endeavor commenced with Release 14, also recognized as LTE-V, and has since been succeeded by Release 15 and 16, commonly denoted as NR-V2X [2]. Vehicular communications may be facilitated via two main approaches: using the existing cellular network infrastructure, explicitly employing C-V2X Mode 3 or NR-V2X Mode 1, or enabling direct communication between cars in an ad-hoc manner, known as C-V2X Mode 4 or NR-V2X Mode 2 [3]. Within the context of vehicular sidelink communication, cars possess the capability to independently assign and oversee a predetermined quantity of resources for specific time intervals. Radio resource management is accomplished via the implementation of a scheduling technique referred to as sensing-based semi-persistent scheduling (SB-SPS). Although resources are allocated semi-persistent, sicyrehicles can adjust characteristics such as transmission power or MCS. Hence, vehicles can adjust thesecharacteristics to serve various objectives, such as managing traffic congestion or facilitating the transfer of packets of varying sizes [4]. In contrast to the uncomplicated process of adjusting transmission power during current transmissions in the C-V2X sidelink, modifying the MCS presents a more complex challenge due to its potential impact on resource occupancy. This challenge occurs because various MCS configurations alter the quantity of transmission resources, affecting the transmission bandwidth. The factors mentioned above have further implications for calculating power levels in the channel and the functioning of SB-SPS. The latter depends on distinct power measures to determine and allocate the necessary resources. The link adaptation (LA) technique has recently been an interesting discussion in wireless communication technology research and the adaptive modulation and coding scheme (AMC) mechanism [5]. The involvement of machine learning is unavoidable in this matter. For instance, the utilization of a deep convolutional neural network (DCNN) is reported in [6]. The estimated combined channel matrix and the noise standard deviation are used as input features to train the DCNN. In [7], convolutional neural networks (CNNs) are proposed for MCS selection at the time of transmission in time-division-duplex (TDD) systems. The proposed method determines the most suitable modulation and coding scheme (MCS) for future transmission based on the shifts in received signal-to-noise ratio (SNR) with time. In [8], the authors aimed to more precisely manage the transmission rates in CSMA/CA wireless networks by employing MAB algorithms. The authors suggested using the logarithmic values of the transmission rates of MCS levels rather than their nominal values in the computation of their anticipated throughput measures. The authors introduced two multi-armed bandit (MAB) algorithms embracing logarithmic rates and a sliding window in the course of time. The proposed MAB algorithms were also developed to support frame aggregation schemes known in wireless network standards for elevated throughput, such as IEEE 802.11 ax. Another report in [9] discusses QoS-guaranteed AMC for mobile-scalable video multicast.