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.

中文翻译：

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基于深度学习的近场超大规模大规模 MIMO 波束训练

超大规模大规模多输入多输出（XL-MIMO）被认为是下一代通信系统的有前途的技术。为了提高波束赋形增益，基于码本的波束训练在 XL-MIMO 系统中被广泛采用。但在XL-MIMO系统中，近场域扩展，波束训练需要采用近场码本，显着增加了导频开销。为了解决这个问题，我们提出了一种基于深度学习的波束训练方案，其中考虑了近场信道模型和近场码本。具体来说，我们首先利用远场宽波束对应的接收信号来估计最佳近场波束。提出了两种训练方案，即提出的原始神经网络和改进的神经网络。原方案直接根据神经网络的输出估计最优近场码字。相比之下，改进后的方案进行了额外的光束测试，可以显着提高光束训练的性能。最后，仿真结果表明，我们提出的方案可以显着降低近场域的训练开销并实现波束成形增益。