The application of millimeter-wave (mmWave) frequencies is a potential technology for satisfying the continuously increasing need for handling data traffic in highly advanced wireless communications. A substantial challenge presented in mmWave communications is the high path loss. mmWave systems adopt beamforming techniques to overcome this issue. These require robust channel estimation and tracking algorithm for maintenance of an adequate quality of service. In this study, we propose a deep learning-based channel estimation and tracking algorithm for vehicular mmWave communications. More specifically, a deep neural network is leveraged to learn the mapping function between the received omni-beam patterns and mmWave channel with negligible overhead. Following the channel estimation, long short-term memory is leveraged to track the channel. The simulation results demonstrate that the proposed algorithm estimates and tracks the mmWave channel efficiently with negligible training overhead.

中文翻译：

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基于深度学习的毫米波车载通信信道估计与跟踪

毫米波 (mmWave) 频率的应用是一种潜在技术，可满足在高度先进的无线通信中处理数据流量的不断增长的需求。毫米波通信面临的一个重大挑战是高路径损耗。毫米波系统采用波束成形技术来克服这个问题。这些需要稳健的信道估计和跟踪算法来维持足够的服务质量。在这项研究中，我们为车载毫米波通信提出了一种基于深度学习的信道估计和跟踪算法。更具体地说，利用深度神经网络来学习接收到的全方位波束模式和毫米波信道之间的映射函数，开销可忽略不计。在信道估计之后，利用长短期记忆来跟踪信道。