## 2 Related Work  
In this section, we have reviewed the existing work on the analysis, network optimization, and the application of reinforcement learning in NR-V2X systems. [PERSON] et al. proposed an analytical model for evaluating the NR-V2X communication performance, focusing on the sensor-based semi-persistent scheduling operations defined in NR-V2X Mode 2 and comparing them with LTE-V2X Mode 4. For different physical layer specifications, the average packet success probability for LTE-V2X and NR-V2X was analyzed, and a moment matching approximation method was used to approximate the SINR statistics under the Nakagami-lognormal composite channel model. It was shown that, under conditions of relatively large inter-vehicle spacing and a high number of vehicles, NR-V2X outperforms LTE-V2X [16]. [PERSON] et al. evaluated and compared the PHY layer performance of various V2V communication technologies. The results showed that NR-V2V outperforms other standards (such as IEEE 802.11 bd) in terms of reliability, range, delay, and data rate. However, under the same modulation and coding scheme, IEEE 802.11 bd performs better in terms of PER. Although the lowest MCS of NR-V2X is more reliable than IEEE 802.11 bd, IEEE 802.11 bd has a wider range. Overall, NR-V2X performs best in V2V communication [17]. Ref. [18] investigated the energy consumption and AoI of a device that is a single-source node, considering potential transmission failures due to poor channel conditions from the source node to the receiver. For a threshold-based retransmission strategy in the system, the corresponding closed-form expressions for average AoI and energy consumption were derived, which can be used to estimate channel failure probabilities and maximum retransmission attempts. Authors of [19] adopted the Truncated Automatic Repeat Request scheme, where terminal devices repeatedly send the current status update until reaching the maximum allowable transmission attempts or generating a new status update. Closed-form expressions for average AoI, average peak AoI, and average energy consumption are derived based on the evolution process of AoI. Authors of [20] primarily considered scenarios where multiple information sources are needed to transmit information for completing status updates, thereby reducing AoI. It investigated the problem of packet scheduling based on information freshness for application-oriented scenarios with correlated multiple information sources. Specifically, it employs AoI to characterize the freshness of status updates for applications and formulates the application-oriented scheduling problem as an MDP problem, utilizing DRL for solving. [PERSON] et al. proposed an implementation method for an Integrated Sensing and Communication (ISAC) system for vehicular networks, addressing the potential performance degradation caused by the coexistence of millimeter-wave radar and communication by extending NR-V2X Mode 2. By using semi-persistent scheduling (SPS) resource selection and dynamically adjusting the radar scanning cycle and transmission power of each vehicle based on the speed and channel congestion status reported by neighboring vehicles, the ISAC system ensures that high-priority vehicles occupy spectrum resources preferentially. Simulation results validated the effectiveness of this approach in improving radar and communication performance, and the ISAC system could better coordinate the coexistence of radar and communication functions, improving the overall performance and security of vehicular networks [21]. [PERSON] et al. proposed a scheme for SL resource allocation in NR-V2X based on 5G cellular mobile communication networks in Mode 1. By using hybrid spectrum access technology and periodic reporting of channel state information, SL resource allocation was modeled as a mixed binary integer nonlinear programming problem to maximize the total throughput of NR-V2X networks among different subcarriers while complying with total available power and minimum transmission rate constraints.