As more and more people choose high-speed rail (HSR) as a means of transportation for short trips, there is ever growing demand of high quality of multimedia services. With its rich spectrum resources, millimeter wave (mm-wave) communications can satisfy the high network capacity requirements for HSR. Also, it is possible for receivers (RXs) to be equipped with antenna arrays in mm-wave communication systems due to its short wavelength. However, as HSRs run with high speed, the received signal power (RSP) varies rapidly over a cell and it is the lowest at the edge of the cell compared to other locations. Consequently, it is necessary to conduct research on RX beamforming for HSR in mm-wave band to improve the quality of the received signal. In this paper, we focus on RX beamforming for a mm-wave train-ground communication system. To improve the RSP, we propose an effective RX beamforming scheme based on deep reinforcement learning (DRL), and develop a deep Q-network (DQN) algorithm to train and determine the optimal RX beam direction with the purpose of maximizing average RSP. Through extensive simulations, we demonstrate that the proposed scheme has better performance than the four baseline schemes in terms of average RSP at most positions on the railway.
翻译:由于越来越多的人选择高速铁路作为短途旅行的交通工具,对高质量多媒体服务的需求不断增加,因此,由于频谱资源丰富,毫米波通信可以满足高频通信的网络能力要求。此外,由于短波长,接收器(RX)有可能在毫米波通信系统中安装天线阵列。然而,随着HSR高速运行,接收的信号能量(RSP)在细胞上变化迅速,与其他地点相比,在细胞边缘是最低的。因此,有必要对以毫米波波波波为HSR进行RX波形成型研究,以提高所接收信号的质量。此外,在本文件中,我们注重RX波成型,用于毫米波地面通信系统。为了改进RSP,我们提议基于深度加固学习(DRL)的有效RX成型系统,并开发一个深Q网络(DQN)算法,用于培训和确定RX最优的RX型波波波幅,从而在最大程度上展示了RSP的铁路平均模拟计划。