Huge overhead of beam training imposes a significant challenge in
millimeter-wave (mmWave) wireless communications. To address this issue, in
this paper, we propose a wide beam based training approach to calibrate the
narrow beam direction according to the channel power leakage. To handle the
complex nonlinear properties of the channel power leakage, deep learning is
utilized to predict the optimal narrow beam directly. Specifically, three deep
learning assisted calibrated beam training schemes are proposed. The first
scheme adopts convolution neural network to implement the prediction based on
the instantaneous received signals of wide beam training. We also perform the
additional narrow beam training based on the predicted probabilities for
further beam direction calibrations. However, the first scheme only depends on
one wide beam training, which lacks the robustness to noise. To tackle this
problem, the second scheme adopts long-short term memory (LSTM) network for
tracking the movement of users and calibrating the beam direction according to
the received signals of prior beam training, in order to enhance the robustness
to noise. To further reduce the overhead of wide beam training, our third
scheme, an adaptive beam training strategy, selects partial wide beams to be
trained based on the prior received signals. Two criteria, namely, optimal
neighboring criterion and maximum probability criterion, are designed for the
selection. Furthermore, to handle mobile scenarios, auxiliary LSTM is
introduced to calibrate the directions of the selected wide beams more
precisely. Simulation results demonstrate that our proposed schemes achieve
significantly higher beamforming gain with smaller beam training overhead
compared with the conventional and existing deep-learning based counterparts.

本篇论文提出了一种基于宽波束训练的窄波束校准方法，通过深度学习预测最佳的窄波束方向，具有很高的波束成形增益和更小的波束训练开销。

毫米波通信系统深度学习辅助校准波束训练

Deep Learning Assisted Calibrated Beam Training for Millimeter-Wave  Communication Systems

A reconfigurable intelligent surface (RIS) can shape the radio propagation by
passively changing the directions of impinging electromagnetic waves. The
optimal control of the RIS requires perfect channel state information (CSI) of
all the links connecting the base station (BS) and the mobile station (MS) via
the RIS. Thereby the channel (parameter) estimation at the BS/MS and the
related message feedback mechanism are needed. In this paper, we adopt a
two-stage channel estimation scheme for the RIS-aided millimeter wave (mmWave)
MIMO channels using an iterative reweighted method to sequentially estimate the
channel parameters. We evaluate the average spectrum efficiency (SE) and the
RIS beamforming gain of the proposed scheme and demonstrate that it achieves
high-resolution estimation with the average SE comparable to that with perfect
CSI.

采用迭代加权方法对 RIS 辅助下的毫米波 MIMO 信道进行了两级渠道估计，评估了所提出的方案的平均频谱效率和 RIS 波束成型增益，并证明了它实现了高分辨率估计，且平均频谱效率比具有完美 CSI 的方案具有可比性。