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

中文翻译：

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架空信使线上的毫米波通信：基于深度强化学习的预测波束跟踪


本文讨论了针对架空通讯线上毫米波 (mmWave) 节点动态进行波束跟踪的可行性。作为在线部署中的特定干扰，我们考虑风力扰动和由脉冲力对电线造成的干扰。我们的贡献是回答考虑到复杂的在线动态，毫米波节点的历史位置/速度是否有助于跟踪定向波束。为此，我们实施深度强化学习（DRL）来学习历史位置/速度与适当的光束转向角度之间的关系。我们的数值评估得出了以下关键见解：首先，针对风扰动，可以从节点的历史位置/速度中学习适当的波束跟踪策略。其次，针对线的冲击力，由于节点的快速位移，节点的位置/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交互。这是通过利用线路上多个点的位置/速度作为 DRL 中的状态信息来完成的。结果证实可以避免由于脉冲力而导致的梁失准，而仅使用节点的位置/速度是不可能实现这一点的。