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首页> 外文期刊>IEEE Transactions on Cognitive Communications and Networking >Autoencoder Neural Network Based Intelligent Hybrid Beamforming Design for mmWave Massive MIMO Systems
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Autoencoder Neural Network Based Intelligent Hybrid Beamforming Design for mmWave Massive MIMO Systems

机译：基于AutoEncoder神经网络的MMWAVE大型MIMO系统基于智能混合波束形成设计

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摘要

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).
机译：混合波束成形（HB）是毫米波（MMWAVE）大规模多输入 - 多输出（MIMO）系统的有希望的技术，其为下一代通信系统提供了低复杂性的高数据容量。然而，由于模拟移位器阵列的硬件约束，数字和模拟波束形成器的关节设计是非凸优化问题。为解决此问题，我们提出了一种基于本文基于AutoEncoder（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，解决原始非凸优化问题的解决是转换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Hyper-参数的选择并为AE神经网络HB设计提供了指导。随着深度神经网络的强烈代表能力，所提出的智能HB在塔式错误率（BER）方面表现出卓越的性能。

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