论文标题
深钢筋学习协调的接收器边界成形用于毫米波火车地面通信
Deep Reinforcement Learning Coordinated Receiver Beamforming for Millimeter-Wave Train-ground Communications
论文作者
论文摘要
随着越来越多的人选择高铁（HSR）作为短途旅行的运输方式，对多媒体服务的高质量需求越来越多。凭借其丰富的光谱资源，毫米波（MM-WAVE）通信可以满足HSR的高网络容量要求。同样，由于其短波长，接收器（RX）可能在MM波通信系统中配备天线阵列。但是，随着HSR高速运行，接收的信号功率（RSP）在单元格上迅速变化，与其他位置相比，它是电池边缘最低的。因此，有必要对MM波频段中HSR的RX光束进行研究，以提高接收信号的质量。在本文中，我们专注于MM波火车地面通信系统的RX横梁成形。为了改善RSP，我们提出了一个基于深钢筋学习（DRL）的有效RX束化方案，并开发了深层Q-NETWORK（DQN）算法来训练并确定最佳的RX光束方向，以最大化平均RSP。通过大量的模拟，我们证明了所提出的方案在铁路上的大多数位置上的平均RSP具有比四个基线方案更好的性能。
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.
