Hybrid beamforming (HB) based millimeter wave (mm-Wave) transmission is a promising technology for providing low latency and high data rate transmission. However, due to the high path loss, the performance of mmWave-based HB is significantly impacted by channel degradation, especially when the number of the available transmission path is less than the transmission streams. Driven by deep learning (DL) based beamforming, we propose a non-orthogonal precoding based HB scheme for the deterioration of channel transmission. Instead of directly applying the neural network as the beamforming function, we present the insight and theoretical analysis of the beamforming strategy in the DL based HB. By studying the beamforming behavior, we designed and formulated deep learning driven hybrid beamforming method. Compared with the existing DL-based hybrid beamforming, the neural network in the proposed method acts as a general neural codebook, which avoids network training even channel changed. In the overloaded channel condition, the proposed deep learning driven non-orthogonal precoding can solve the problem of channel degradation, which improves the robustness for hybrid beamforming. Simulation results show that the proposed method can achieve significant performance improvement over existing ones in overloaded transmission conditions. We also designed and implemented our scheme over the FPGA platform, which shows a much lower hardware cost than other existing schemes.

中文翻译：

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用于毫米波通信的深度学习驱动的非正交预编码


基于混合波束成形 (HB) 的毫米波 (mm-Wave) 传输是一种很有前途的技术，可提供低延迟和高数据速率传输。然而，由于路径损耗较高，基于毫米波的HB的性能受到信道退化的显着影响，特别是当可用传输路径的数量少于传输流时。在基于深度学习（DL）的波束成形的驱动下，我们提出了一种基于非正交预编码的 HB 方案来解决信道传输的恶化问题。我们没有直接应用神经网络作为波束形成函数，而是提出了基于 DL 的 HB 中波束形成策略的见解和理论分析。通过研究波束形成行为，我们设计并制定了深度学习驱动的混合波束形成方法。与现有的基于深度学习的混合波束形成相比，该方法中的神经网络充当通用神经码本，避免了网络训练甚至信道改变。在过载信道条件下，所提出的深度学习驱动的非正交预编码可以解决信道退化问题，从而提高混合波束成形的鲁棒性。仿真结果表明，在过载传输条件下，所提出的方法比现有方法能够取得显着的性能提升。我们还在FPGA平台上设计并实现了我们的方案，这表明比其他现有方案的硬件成本低得多。