Hybrid beamforming (HB) is a promising technology for the millimeter-wave (mmWave) massive multiple-input-multiple-output (MIMO) system, which supplies high data capacity with low complexity for next-generation communication systems. However, the joint design of digital and analog beamformer is a non-convex optimization problem due to the hardware constraints of analog shifter arrays. To address this issue, we proposed an intelligent HB design method based on the autoencoder (AE) neural network in this paper. By mapping the HB system to an AE neural network, the solving of the original non-convex optimization problem is converted to the neural network training process. The beamformer and combiner can be automatically formulated by the training process of the neural network. We also discuss the chosen of hyper-parameter and provide a guideline for the AE neural network HB design. With the strong representation ability of the deep neural network, the proposed intelligent HB exhibits superior performance in terms of bit error rate (BER).

中文翻译：

---

基于自编码器神经网络的毫米波大规模 MIMO 系统的智能混合波束成形设计

混合波束成形 (HB) 是一种用于毫米波 (mmWave) 大规模多输入多输出 (MIMO) 系统的有前途的技术，可为下一代通信系统提供高数据容量和低复杂度。然而，由于模拟移位器阵列的硬件限制，数字和模拟波束形成器的联合设计是一个非凸优化问题。为了解决这个问题，我们在本文中提出了一种基于自动编码器 (AE) 神经网络的智能 HB 设计方法。通过将HB系统映射到AE神经网络，将原来非凸优化问题的求解转化为神经网络训练过程。波束形成器和组合器可以通过神经网络的训练过程自动制定。我们还讨论了超参数的选择，并为 AE 神经网络 HB 设计提供了指导。凭借深度神经网络的强大表示能力，所提出的智能 HB 在误码率 (BER) 方面表现出卓越的性能。