In this paper, we propose a deep learning-based beam tracking method for millimeter-wave (mmWave) communications. Beam tracking is employed for transmitting the known symbols using the sounding beams and tracking time-varying channels to maintain a reliable communication link. When the pose of a user equipment (UE) device varies rapidly, the mmWave channels also tend to vary fast, which hinders seamless communication. Thus, models that can capture temporal behavior of mmWave channels caused by the motion of the device are required, to cope with this problem. Accordingly, we employ a deep neural network to analyze the temporal structure and patterns underlying in the time-varying channels and the signals acquired by inertial sensors. We propose a model based on long short term memory (LSTM) that predicts the distribution of the future channel behavior based on a sequence of input signals available at the UE. This channel distribution is used to 1) control the sounding beams adaptively for the future channel state and 2) update the channel estimate through the measurement update step under a sequential Bayesian estimation framework. Our experimental results demonstrate that the proposed method achieves a significant performance gain over the conventional beam tracking methods under various mobility scenarios.

中文翻译：

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基于深度学习的移动毫米波通信波束跟踪

在本文中，我们提出了一种用于毫米波 (mmWave) 通信的基于深度学习的波束跟踪方法。使用波束跟踪传输已知符号探测波束和跟踪时变信道以保持可靠的通信链路。当用户设备（UE）设备的姿态快速变化时，毫米波信道也趋于快速变化，这阻碍了无缝通信。因此，需要能够捕捉由设备运动引起的毫米波信道时间行为的模型来解决这个问题。因此，我们采用深度神经网络来分析时变通道中的时间结构和模式以及惯性传感器获取的信号。我们提出了一个基于长短期记忆 (LSTM) 的模型，该模型基于 UE 可用的输入信号序列预测未来信道行为的分布。顺序贝叶斯估计框架下的测量更新步骤。我们的实验结果表明，在各种移动性场景下，所提出的方法比传统的波束跟踪方法获得了显着的性能提升。