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.
翻译:光束培训的巨头在毫米波(mmWave)无线通信中提出了巨大的挑战。为了解决这个问题,我们在本文件中提议采用一个广梁培训方法,根据频道功率渗漏校准窄梁方向。为了处理频道功率渗漏的复杂非线性特性,将利用深层学习直接预测最优窄梁。具体地说,提出了三个深层次学习辅助校准光束培训方案。第一个方案采用同声神经网络,根据即时收到的广梁培训信号进行预测。我们还根据预测的波束方向校准进行额外的窄线培训。然而,第一个方案只依靠一个宽梁培训,而这种培训缺乏声音的稳健性能。第二个方案采用长期短期内存(LSTM)网络来跟踪用户的移动情况,并根据事先培训的信号调整波束方向,以加强噪音的稳健性。为了进一步降低广度培训的顶部,我们第三个方案仅取决于一个宽波束训练的概率,即根据目前最精确的轨道标准进行调整。