Wang Jizhe, Zheng Yuanbing, Wang Jian, Shen Zhenghua, Tong Lei, Jing Yahao, Luo Yu, Liao Yong
State Grid Chongqing Information and Telecommunication Company, Chongqing 400012, China.
School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China.
Sensors (Basel). 2023 Oct 13;23(20):8454. doi: 10.3390/s23208454.
Higher standards for reliability and efficiency apply to the connection between vehicle terminals and infrastructure by the fifth-generation mobile communication technology (5G). A vehicle-to-infrastructure system uses a communication system called NR-V2I (New Radio-Vehicle to Infrastructure), which uses Link Adaptation (LA) technology to communicate in constantly changing V2I to increase the efficacy and reliability of V2I information transmission. This paper proposes a Double Deep Q-learning (DDQL) LA scheduling algorithm for optimizing the modulation and coding scheme (MCS) of autonomous driving vehicles in V2I communication. The problem with the Doppler shift and complex fast time-varying channels reducing the reliability of information transmission in V2I scenarios is that they make it less likely that the information will be transmitted accurately. Schedules for autonomous vehicles using Space Division Multiplexing (SDM) and MCS are used in V2I communications. To address the issue of Deep Q-learning (DQL) overestimation in the Q-Network learning process, the approach integrates Deep Neural Network (DNN) and Double Q-Network (DDQN). The findings of this study demonstrate that the suggested algorithm can adapt to complex channel environments with varying vehicle speeds in V2I scenarios and by choosing the best scheduling scheme for V2I road information transmission using a combination of MCS. SDM not only increases the accuracy of the transmission of road safety information but also helps to foster cooperation and communication between vehicle terminals to realize cooperative driving.
第五代移动通信技术（5G）对车辆终端与基础设施之间的连接提出了更高的可靠性和效率标准。车路协同系统使用一种名为NR-V2I（新无线电车路协同）的通信系统，该系统采用链路自适应（LA）技术在不断变化的车路协同环境中进行通信，以提高车路协同信息传输的效率和可靠性。本文提出了一种双深度Q学习（DDQL）LA调度算法，用于优化车路协同通信中自动驾驶车辆的调制与编码方案（MCS）。在车路协同场景中，多普勒频移和复杂的快速时变信道会降低信息传输的可靠性，问题在于信息准确传输的可能性变小。车路协同通信中使用了基于空分复用（SDM）和MCS的自动驾驶车辆调度方案。为了解决深度Q学习（DQL）在Q网络学习过程中的高估问题，该方法将深度神经网络（DNN）和双Q网络（DDQN）相结合。本研究结果表明，所提出的算法能够适应车路协同场景中不同车速的复杂信道环境，并通过结合MCS为车路协同道路信息传输选择最佳调度方案。SDM不仅提高了道路安全信息传输的准确性，还有助于促进车辆终端之间的合作与通信，以实现协同驾驶。
