5G millimeter wave (mmWave) communication is an efficient technique for low delay and high data rate transmission in vehicular networks. Due to the high path loss in 5G mmWave band, 5G base stations need to be densely deployed, which may result in great deployment expenditures. In this letter, we jointly consider a relay selection problem in multihop 5G mmWave device to device (D2D) transmissions and a power level allocation problem of mmWave D2D links. We propose a centralized hierarchical deep reinforcement learning based method to find an optimal solution for the problem. The proposed method does not rely on the information of links, and it tries to find an optimal solution based on the information of vehicles. Simulation results show that the convergence of the proposed method, and the transmission delay performance of proposed method is better than a link-quality-prediction based method, and close to a link-quality-known method.

中文翻译：

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毫米波车载网络中基于深度强化学习的 D2D 中继选择和功率电平分配

5G 毫米波 (mmWave) 通信是一种在车载网络中实现低延迟和高数据速率传输的有效技术。由于5G毫米波频段的路径损耗较高，5G基站需要密集部署，可能会导致较大的部署支出。在这封信中，我们共同考虑了多跳 5G 毫米波设备到设备 (D2D) 传输中的中继选择问题和毫米波 D2D 链路的功率水平分配问题。我们提出了一种基于集中式分层深度强化学习的方法来寻找问题的最佳解决方案。所提出的方法不依赖于链路信息，它试图根据车辆信息寻找最优解。仿真结果表明，所提方法的收敛性，