# Reinforcement Learning Approach for Adaptive C-V2X Resource Management
## 1 Introduction
An AMC scheme has been introduced to accommodate both spatial variety and temporal instabilities on multicast channels for providing QoS and enhancing resource utility. An AMC optimization problem that maximizes the resource utility while ensuring each user's QoS has been acquired based on MDPs. The explained recent works mainly discuss the general wireless communication network; nevertheless, some reports also discuss the AMC mechanism in the narrowband internet-of-things (NB-IoT) domain. As reported in [10], the authors targeted the recurrence factor for NB-IoT, which 3 GPP introduces to enhance maximum coupling loss (MCL). The authors addressed downlink 2-dimension link adaptation in NB-IoT. They presented a method for link adaptation to reduce depleted resources and operational time for packet transmission by determining the slightest achievable repetition number and the least potential MCS level. The generative adversarial network (GAN) models are introduced in [11] to manage the stochastic time-stamps of traffic scheduling associated with adaptive MCS values, repetitions, and number of PRBs. One of the limitations of the GAN models is that the system needs to do the periodic re-training to adapt to the network changes. Some reports also highlight the effect of implementing LA and AMC to achieve the ultra-reliable low latency communications (URLLCs) requirement. The mentioned works are reported in [12; 13; 14]. The reports utilize well-known key performance indicators (KPI), such as BLER, throughput, and latency. Nevertheless, the majority of vehicular networks in the reports utilize a roadside unit (RSU) to govern resource management. In this work, we want to investigate the AMC within the autonomous situation as in the 3 GPP mode-4 mechanism. Regarding vehicular networks, especially in cellular communication, the AMC technique is used to be susceptible to particular environmental information. This information includes channel quality indicator (CQI), network latency, block error rate (BLER), and throughput. The report in [15; 16] investigated the AMC method for a massive multiple-input multiple-output (MIMO) scenario. Although not discussing vehicular networks in particular, the reports examine the general idea of adapting the AMC in the MIMO and 5G-MIMO systems, respectively. The use of CQI information to construct the AMC mechanism is presented in some reports, such as in [17; 18; 19; 20; 21]. From our interpretation, despite the majority of the reports posing a real-world configuration, most of the following reports still need to present a discussion of the overall packet received ratio (PRR) performance. The real-time adaptation of the MCS has mainly been suggested to implement distributed congestion control in C-V2X, as indicated by the 3 GPP and relevant scholarly sources. Additionally, it has been recommended as a viable remedy for transmitting packets of varying sizes, although to a lesser degree [22]. Nevertheless, related research in this field has primarily examined the effects of various fixed MCS configurations, which do not adhere to the requirements set by the European Telecommunications Standards Institute (ETSI) [23]. Alternatively, other studies have primarily discussed the possibility of MCS adaptation without thoroughly assessing its functionality and effectiveness. The authors briefly examine the use of MCS adaptation for congestion management in C-V2X Mode 4 in references [24; 25]. They also assess the possible reduction in congestion by raising the MCS, which leads to a fall in performance but a decrease in necessary resources. Nevertheless, these studies need to examine the potential consequences of incorporating this mechanism into the functioning of C-V2X SB-SPS, a significant factor in its overall performance. The research in reference [26] comprehensively examines various congestion management methods in the context of C-V2X communication. The study also investigates the influence of different MCS configurations on the performance of C-V2X in vehicle congestion.