arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2012.00982cs.NI
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基于深度强化学习的毫米波通信预测波束跟踪

Millimeter Wave Communications on Overhead Messenger Wire: Deep Reinforcement Learning-Based Predictive Beam Tracking

Yusuke Koda, Masao Shinzaki, Koji Yamamoto, Takayuki Nishio, Masahiro Morikura, Yushi Shirato, Daisei Uchida, Naoki Kita

摘要:

本文讨论了波束跟踪的可行性,对动力学毫米波(毫米波)节点放置在架空的承力索,包括风强迫扰动和冲击力对导线造成的干扰。我们的主要贡献是回答是否历史位置和速度的毫米波节点是有用的跟踪定向波束给定复杂的在线动态。为此,我们实现基于深度强化学习(DRL)的波束跟踪,以学习历史位置/速度与适当波束转向角之间的复杂关系。我们的数值评估产生了以下关键的见解:对风的扰动,一个适当的光束跟踪政策,可以从一个节点的历史位置和速度。同时,由于节点的快速位移,抵抗对线的冲击力,节点的位置和速度的使用不一定足够。为了解决这个问题,我们建议利用线上的位置相互作用,利用线上的几个点的位置/速度作为DRL中的状态信息。结果证实,这导致避免了波束失准,这通过仅使用节点的位置/速度是不可能的。

英文摘要:

This paper discusses the feasibility of beam tracking against dynamics in millimeter wave (mmWave) nodes placed on overhead messenger wires, including wind-forced perturbations and disturbances caused by impulsive forces to wires. Our main contribution is to answer whether or not historical positions and velocities of a mmWave node is useful to track directional beams given the complicated on-wire dynamics. To this end, we implement beam-tracking based on deep reinforcement learning (DRL) to learn the complicated relationships between the historical positions/velocities and appropriate beam steering angles. Our numerical evaluations yielded the following key insights: Against wind perturbations, an appropriate beam-tracking policy can be learned from the historical positions and velocities of a node. Meanwhile, against impulsive forces to the wire, the use of the position and velocity of the node is not necessarily sufficient owing to the rapid displacement of the node. To solve this, we propose to take advantage of the positional interaction on the wire by leveraging the positions/velocities of several points on the wire as state information in DRL. The results confirmed that this results in the avoidance of beam misalignment, which would not be possible by using only the position/velocity of the node.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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