Autonomous driving may be the most important application scenario of next generation, the development of wireless access technologies enabling reliable and low-latency vehicle communication becomes crucial. To address this, 3GPP 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 advancement in cellular V2X (C-V2X) with improved performance of NR-V2X. However, in NR-V2X Mode 2, resource collisions still occur, and thus degrade the age of information (AOI). Therefore, a 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 interval (RRI), higher-frequency transmissions take ore 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 have demonstrated the performance of our proposed algorithm.

翻译：自动驾驶可能是下一代最重要的应用场景，发展能够实现可靠低延迟车辆通信的无线接入技术变得至关重要。为此，3GPP基于5G新空口技术制定了车联网通信规范，其中模式2侧链路通信类似于LTE-V2X中的模式4，允许车辆间直接通信。这补充了LTE-V2X中的侧链路通信，代表了蜂窝车联网技术的最新进展，并通过NR-V2X提升了性能。然而在NR-V2X模式2中，资源冲突仍然存在，从而劣化了信息年龄。因此，本文采用干扰消除方法，通过将NR-V2X与非正交多址技术相结合来减轻这种影响。在NR-V2X中，当车辆选择较小的资源预留间隔时，更高频次的传输会消耗更多能量以降低信息年龄。因此，基于NR-V2X通信联合考虑信息年龄与通信能耗具有重要意义。为此，我们将该问题建模为优化问题，并采用深度强化学习算法计算每辆发射车辆的最优传输资源预留间隔与发射功率，以降低每辆发射车辆的能耗和每辆接收车辆的信息年龄。大量仿真实验验证了所提算法的性能。