###### Abstract  
As autonomous driving may be the most important application scenario of the next generation, the development of wireless access technologies enabling reliable and low-latency vehicle communication becomes crucial. To address this, 3 GPP has developed Vehicle-to-Everything (V2X) specifications based on 5G New Radio (NR) technology, where Mode 2 Side-Link (SL) communication resembles Mode 4 in LTE-V2X, allowing direct communication between vehicles. This supplements SL communication in LTE-V2X and represents the latest advancements in cellular V2X (C-V2X) with the improved performance of NR-V2X. However, in NR-V2X Mode 2, resource collisions still occur and thus degrade the age of information (AOI). Therefore, an interference cancellation method is employed to mitigate this impact by combining NR-V2X with Non-Orthogonal multiple access (NOMA) technology. In NR-V2X, when vehicles select smaller resource reservation intervals (RRIs), higher-frequency transmissions use more energy to reduce AoI. Hence, it is important to jointly consider AoI and communication energy consumption based on NR-V2X communication. Then, we formulate such an optimization problem and employ the Deep Reinforcement Learning (DRL) algorithm to compute the optimal transmission RRI and transmission power for each transmitting vehicle to reduce the energy consumption of each transmitting vehicle and the AoI of each receiving vehicle. Extensive simulations demonstrate the performance of our proposed algorithm. 1  
2024 24 4388 1222 22  
3 GPP has formulated V2X specifications based on 5G NR technology to support ultra-low latency and ultra-high reliability in evolving vehicle applications, communications, and service requirements [11]. As pointed out in [12], the development of SL in NR-V2X is to supplement and expand the SL communication in LTE-V2X. However, the autonomous resource allocation method used in Mode 2 still suffers from resource collisions. When the RRI decreases and the vehicle occupies more resources, the collision probability gradually increases. Collisions mean one vehicle receiving multiple messages simultaneously, which results in the mutual interference between these messages and directly reduces the Signal-to-Interference-plus-Noise Ratio (SINR) of each message, which degrades the transmission time. Therefore, NOMA is used to mitigate the impact of this situation. When the vehicle receives multiple collision messages, NOMA decodes these messages separately to increase the SINR of messages with relatively low power and improve communication performance. Furthermore, as mentioned in [13], while ensuring communication effectiveness, energy consumption also needs to be considered. The transmission power also affects the SINR and energy consumption during the transmission process [14]. When the power is high, the SINR is more likely to meet the requirements for successful transmission, thereby reducing AoI, but energy consumption will also increase at the same time [15]. Therefore, in NR-V2X communication, it is necessary to comprehensively consider the balance between communication effectiveness and energy consumption. In order to address this issue, a resource allocation scheme based on DRL is proposed to allocate RRI and power for vehiclesensuring low energy consumption and low information age of the system during the communication process (The source code has been released at the following link: [[https://github.com/qiongwu86/joint-Optimization-of-AoI-and-Energy-Consumption-in-NR-V2X-System-based-on-DRL](https://github.com/qiongwu86/joint-Optimization-of-AoI-and-Energy-Consumption-in-NR-V2X-System-based-on-DRL)]([https://github.com/qiongwu86/joint-Optimization-of-AoI-and-Energy-Consumption-in-NR-V2X-System-based-on-DRL](https://github.com/qiongwu86/joint-Optimization-of-AoI-and-Energy-Consumption-in-NR-V2X-System-based-on-DRL)) accessed on 6 June 2024).