基于深度强化学习的NR-V2X系统中信息年龄与能耗的联合优化
Joint Optimization of Age of Information and Energy Consumption in NR-V2X System based on Deep Reinforcement Learning
AI总结：
针对NR-V2X Mode 2中资源冲突导致信息年龄恶化及传输能耗问题，提出基于深度强化学习的联合优化方法，计算最优传输间隔和功率，降低能耗与信息年龄。
摘要：
自动驾驶可能是下一代最重要的应用场景，开发能够实现可靠且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新无线电（NR）技术制定了车辆到一切（V2X）规范，其中Mode 2侧行链路（SL）通信类似于LTE-V2X中的Mode 4，允许车辆之间直接通信。这补充了LTE-V2X中的SL通信，代表了蜂窝V2X（C-V2X）的最新进展，并提升了NR-V2X的性能。然而，在NR-V2X Mode 2中，资源冲突仍然发生，从而降低了信息年龄（AOI）。因此，采用一种干扰消除方法，通过将NR-V2X与非正交多址（NOMA）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（RRI）时，更高频率的传输需要更多能量来降低AoI。因此，基于NR-V2X通信联合考虑AoI和通信能耗非常重要。然后，我们制定了这样一个优化问题，并采用深度强化学习（DRL）算法为每辆发送车辆计算最优传输RRI和传输功率，以降低每辆发送车辆的能量消耗和每辆接收车辆的AoI。大量仿真证明了我们提出算法的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
