Autoencoder Neural Network Based Intelligent Hybrid Beamforming Design for mmWave Massive MIMO Systems
Autoencoder Neural Network Based Intelligent Hybrid Beamforming Design for mmWave Massive MIMO Systems
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DOI：
10.1109/tccn.2020.2991878
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发表时间：
2020-09-01
影响因子：
8.6
通讯作者：
Xie, Junfei
中科院分区：
文献类型：
--
作者：
Tao, Jiyun;Chen, Jienan;Xie, Junfei
混合波束形成（HB）技术是毫米波（mmWave）大规模多输入多输出（MIMO）系统中的一种很有前途的技术，它为下一代通信系统提供了低复杂度的高数据容量。然而，由于模拟移相器阵列的硬件限制，数字和模拟波束形成器的联合设计是一个非凸优化问题。针对这一问题，提出了一种基于自编码器（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，将原非凸优化问题的求解转化为神经网络的训练过程。波束形成器和组合器可以通过神经网络的训练过程自动地公式化。讨论了超参数的选取，为AE神经网络HB的设计提供了指导。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）方面表现出上级的性能。
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).