Extremely large-scale massive multiple-input-multiple-output (XL-MIMO) is regarded as a promising technology for next-generation communication systems. In order to enhance the beamforming gains, codebook-based beam training is widely adopted in XL-MIMO systems. However, in XL-MIMO systems, the near-field domain expands, and near-field codebook should be adopted for beam training, which significantly increases the pilot overhead. To tackle this problem, we propose a deep learning-based beam training scheme where the near-field channel model and the near-field codebook are considered. To be specific, we first utilize the received signals corresponding to the far-field wide beams to estimate the optimal near-field beam. Two training schemes are proposed, namely the proposed original and the improved neural networks. The original scheme estimates the optimal near-field codeword directly based on the output of the neural networks. By contrast, the improved scheme performs additional beam testing, which can significantly improve the performance of beam training. Finally, the simulation results show that our proposed schemes can significantly reduce the training overhead in the near-field domain and achieve beamforming gains.
翻译:在XL-MIMO系统中,广泛采用基于代码书的波束培训,但在XL-MIMO系统中,应采用近地域扩展和近地代码簿来进行光束培训,这大大增加了实验性间接费用。为解决这一问题,我们提议采用一个深层次的基于学习的波束培训计划,考虑近地频道模式和近地代码簿。具体地说,我们首先利用远地光束接收到的信号来估计最佳的近地光束。提出了两个培训计划,即拟议的原始和改良的神经网络。原计划估计直接以神经网络产出为基础的最佳近地代码词。相比之下,改进后的计划进行额外的波束测试,可以大大改进波形培训的绩效。最后,模拟结果显示,我们拟议的计划可以大大减少近地光束培训,并在近地进行。