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arXiv 2203.02196cs.ITeess.SPmath.IT
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基于MMSE模型的多用户MIMO神经预编码算法

Data Augmentation Empowered Neural Precoding for Multiuser MIMO with MMSE Model

Shaoqing Zhang, Jindan Xu, Wei Xu, NingWang, Derrick Wing Kwan Ng, Xiaohu You

摘要:

利用深度学习方法的预编码设计已经被广泛研究用于多用户多输入多输出(MU-MIMO)系统。然而,传统的神经预编码设计应用基于黑盒的神经网络,其不太可解释。在本文中,我们提出了一种基于深度学习的预编码方法,该方法基于神经预编码网络的可解释设计,即iPNet。特别地,iPNet模仿经典的最小均方误差(MMSE)预编码,并且在神经网络架构的设计中近似矩阵求逆。具体而言,建议的iPNet由一个模型驱动的组件网络,负责增强输入信道状态信息(CSI),和一个数据驱动的子网络,负责从这个增强的CSI预编码计算。后一个数据驱动模块被明确地解释为MMSE预编码器的无监督学习者。仿真结果表明,通过利用增强的CSI,所提出的iPNet比现有的黑盒设计实现了显着的性能提升,并且还表现出针对CSI不匹配的增强的通用性。

英文摘要:

Precoding design exploiting deep learning methods has been widely studied for multiuser multiple-input multiple-output (MU-MIMO) systems. However, conventional neural precoding design applies black-box-based neural networks which are less interpretable. In this paper, we propose a deep learning-based precoding method based on an interpretable design of a neural precoding network, namely iPNet. In particular, the iPNet mimics the classic minimum mean-squared error (MMSE) precoding and approximates the matrix inversion in the design of the neural network architecture. Specifically, the proposed iPNet consists of a model-driven component network, responsible for augmenting the input channel state information (CSI), and a data-driven sub-network, responsible for precoding calculation from this augmented CSI. The latter data-driven module is explicitly interpreted as an unsupervised learner of the MMSE precoder. Simulation results show that by exploiting the augmented CSI, the proposed iPNet achieves noticeable performance gain over existing black-box designs and also exhibits enhanced generalizability against CSI mismatches.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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