## 2 Related Work
Simulation results showed that the proposed power allocation scheme could save energy, and the suboptimal SL resource allocation algorithm outperformed other methods [22]. [PERSON] et al. conducted an in-depth analysis and evaluated the performance of 5G NR-V2X Mode 2 under different traffic patterns. The study pointed out that additional reselections could make SPS more unstable and prone to collisions. Moreover, frequent resource reselections could increase implementation costs. Therefore, they proposed suggestions for an adjusted reevaluation mechanism to reduce implementation costs and improve system performance. This work provided valuable insights for the further development and optimization of NR-V2X Mode 2 [23]. [PERSON] et al. focused on the joint energy efficiency and total rate maximization problem of autonomous resource selection in NR-V2X vehicle communication to meet reliability and latency requirements. They formulated the autonomous resource allocation problem as the ratio of total rate to energy consumption, aiming to maximize the total energy efficiency of power-saving users under reliability and latency requirements. Since the energy efficiency problem is a complex mixed integer programming problem, a traffic-based resource allocation density heuristic algorithm was proposed to address this problem, ensuring the same successful transmission rate as perception-based algorithms while improving energy efficiency by reducing the power consumption per user [24]. [PERSON] et al. focused on the efficiency of radio resource allocation and scheduling algorithms in C-V2X communication networks and their impact on latency and reliability. Due to the continuous movement of vehicles, perception-based SPS becomes ineffective, leading to noneffective resource allocation and frequent resource conflicts. Therefore, the C-V2X communication network was described as a decentralized multi-agent network Markov decision process. Two variants, independent actor-critic and shared experience actor-critic, were proposed, achieving a 15-20% improvement in reception probability in high vehicle density scenarios [25]. [PERSON] et al. considered optimizing the medium access control layer in NR-V2X for more effective congestion control. They took into account the AoI indicator in the optimization process and introduced DRL to manage packet transmission rate and transmission power while ensuring high throughput. Compared with traditional distributed congestion control algorithms, the proposed solution demonstrated better performance in terms of timeliness, throughput, and average CBR. It highlights the importance and effectiveness of DRL-based congestion control mechanisms in the context of AoI [26]. Currently, NOMA has been applied in many scenarios. [PERSON] et al. introduced an energy-efficient sub-channel and power allocation strategy for URLLC-enabled GF-NOMA systems using multi-agent Deep Reinforcement Learning (MADRL). It aims to maximize network energy efficiency while meeting URLLC requirements. It used simulation to check MA2 DQN and MADQN performances [27]. [PERSON] et al. explored secure offloading in vehicular edge computing (VEC) networks with malicious eavesdroppers using NOMA. An A3C-based scheme was proposed to optimize energy consumption and computation delay. Simulation results demonstrated its advantage in terms of energy efficiency and security [28]. [PERSON] et al. focused on VEC systems, where tasks can be processed locally or offloaded based on vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication. It employed decentralized DRL and the deep deterministic policy gradient (DDPG) algorithm to perform the power allocation while addressing the uncertainty of MIMO-NOMA-based V2I communication and random task arrivals [29]. In summary, the existing works in the literature on NR-V2X performance analysis and research have not considered the impact of NOMA on AoI. They also have not considered employing DRL in optimizing AoI and energy consumption in NOMA and NR-V2X based vehicular networks.