Song Shulin, Zhang Zheng, Wu Qiong, Fan Pingyi, Fan Qiang
School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China.
Zhuhai Fudan Innovation Institute, Zhuhai 519031, China.
Sensors (Basel). 2024 Jul 4;24(13):4338. doi: 10.3390/s24134338.
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, 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 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 considerAoI 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.
由于自动驾驶可能是下一代最重要的应用场景，因此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。为了解决这个问题，3GPP基于5G新无线电（NR）技术开发了车联网（V2X）规范，其中模式2侧链路（SL）通信类似于LTE-V2X中的模式4，允许车辆之间直接通信。这补充了LTE-V2X中的SL通信，并代表了蜂窝车联网（C-V2X）的最新进展，具有改进的NR-V2X性能。然而，在NR-V2X模式2中，资源冲突仍然会发生，从而降低信息年龄（AOI）。因此，采用一种干扰消除方法，通过将NR-V2X与非正交多址接入（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，高频传输会消耗更多能量来降低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）算法为每个发射车辆计算最优传输RRI和传输功率，以降低每个发射车辆的能耗和每个接收车辆的AOI。大量仿真验证了我们提出的算法的性能。
