To address communication latency issues, the Third Generation Partnership Project (3GPP) has defined Cellular-Vehicle to Everything (C-V2X) technology, which includes Vehicle-to-Vehicle (V2V) communication for direct vehicle-to-vehicle communication. However, this method requires vehicles to autonomously select communication resources based on the Semi-Persistent Scheduling (SPS) protocol, which may lead to collisions due to different vehicles sharing the same communication resources, thereby affecting communication effectiveness. Non-Orthogonal Multiple Access (NOMA) is considered a potential solution for handling large-scale vehicle communication, as it can enhance the Signal-to-Interference-plus-Noise Ratio (SINR) by employing Successive Interference Cancellation (SIC), thereby reducing the negative impact of communication collisions. When evaluating vehicle communication performance, traditional metrics such as reliability and transmission delay present certain contradictions. Introducing the new metric Age of Information (AoI) provides a more comprehensive evaluation of communication system. Additionally, to ensure service quality, user terminals need to possess high computational capabilities, which may lead to increased energy consumption, necessitating a trade-off between communication energy consumption and effectiveness. Given the complexity and dynamics of communication systems, Deep Reinforcement Learning (DRL) serves as an intelligent learning method capable of learning optimal strategies in dynamic environments. Therefore, this paper analyzes the effects of multi-priority queues and NOMA on AoI in the C-V2X vehicular communication system and proposes an energy consumption and AoI optimization method based on DRL. Finally, through comparative simulations with baseline methods, the proposed approach demonstrates its advances in terms of energy consumption and AoI.

翻译：为解决通信时延问题，第三代合作伙伴计划（3GPP）定义了蜂窝车联网（C-V2X）技术，其中包含车对车（V2V）通信以实现车辆间的直接通信。然而，该方法要求车辆基于半持续调度（SPS）协议自主选择通信资源，可能导致不同车辆共享相同通信资源而产生碰撞，从而影响通信效能。非正交多址接入（NOMA）被视为处理大规模车辆通信的潜在解决方案，因其可通过采用连续干扰消除（SIC）技术提升信号与干扰加噪声比（SINR），从而降低通信碰撞的负面影响。在评估车辆通信性能时，传统指标如可靠性与传输时延存在一定矛盾。引入信息年龄（AoI）这一新指标可为通信系统提供更全面的评估。此外，为保障服务质量，用户终端需具备高计算能力，这可能导致能耗增加，因此需要在通信能耗与效能之间进行权衡。鉴于通信系统的复杂性与动态性，深度强化学习（DRL）作为一种智能学习方法，能够在动态环境中学习最优策略。为此，本文分析了C-V2X车辆通信系统中多优先级队列与NOMA对AoI的影响，并提出一种基于DRL的能耗与AoI优化方法。最后，通过与基线方法的对比仿真，验证了所提方法在能耗与AoI方面的优越性。