论文标题
深度学习辅助MMWave光束预测，并具有先前的低频信息
Deep Learning Assisted mmWave Beam Prediction with Prior Low-frequency Information
论文作者
论文摘要
巨大的梁训练开销对MMWave通信构成了重大挑战。为了解决这个问题，对梁跟踪进行了广泛的研究，而现有方法很难处理严重的多径干扰和非平稳情况。受到非标准酮体系结构中低频和MMWave通道之间的空间相似性的启发，本文提议利用先前的低频信息来预测最佳的MMWave光束，其中采用了深度学习来提高预测准确性。具体而言，应用定期估计的低频通道状态信息（CSI）用于跟踪用户设备的移动，并提出了定时偏移指示器，以指示相对于低频CSI估计的MMWave光束训练的瞬间。同时，基于长期术语内存网络的专用模型旨在实施预测。仿真结果表明，我们所提出的方案可以比传统方法获得更高的光束成型增益，同时几乎不需要MMWave梁训练开销。
Huge overhead of beam training poses a significant challenge to mmWave communications. To address this issue, beam tracking has been widely investigated whereas existing methods are hard to handle serious multipath interference and non-stationary scenarios. Inspired by the spatial similarity between low-frequency and mmWave channels in non-standalone architectures, this paper proposes to utilize prior low-frequency information to predict the optimal mmWave beam, where deep learning is adopted to enhance the prediction accuracy. Specifically, periodically estimated low-frequency channel state information (CSI) is applied to track the movement of user equipment, and timing offset indicator is proposed to indicate the instant of mmWave beam training relative to low-frequency CSI estimation. Meanwhile, long-short term memory networks based dedicated models are designed to implement the prediction. Simulation results show that our proposed scheme can achieve higher beamforming gain than the conventional methods while requiring little overhead of mmWave beam training.
