论文标题
模型驱动的深度学习，用于大型Mu-Mimo，具有有限的阿尔如图
Model-Driven Deep Learning for Massive MU-MIMO with Finite-Alphabet Precoding
论文作者
论文摘要
大量的多源多输入多输出（MU-MIMO）一直是第五代无线系统中的主流技术。为了减少高硬件成本和大量MU-MIMO的功耗，使用下链路传输中每个天线和射频（RF）链的低分辨率数字到Analog转换器（DAC），这为预编码设计带来了挑战。为了避免这些障碍，我们在本文中开发了一个模型驱动的深度学习（DL）网络，用于大规模的MU-MIMO，并具有有限的Alphabet预编码。网络的架构是通过展开迭代算法而专门设计的。与传统的最新技术相比，提出的基于DL的预码器在瑞利褪色通道下对通道估计误差的性能，复杂性和鲁棒性具有显着优势。
Massive multiuser multiple-input multiple-output (MU-MIMO) has been the mainstream technology in fifth-generation wireless systems. To reduce high hardware costs and power consumption in massive MU-MIMO, low-resolution digital-to-analog converters (DAC) for each antenna and radio frequency (RF) chain in downlink transmission is used, which brings challenges for precoding design. To circumvent these obstacles, we develop a model-driven deep learning (DL) network for massive MU-MIMO with finite-alphabet precoding in this article. The architecture of the network is specially designed by unfolding an iterative algorithm. Compared with the traditional state-of-the-art techniques, the proposed DL-based precoder shows significant advantages in performance, complexity, and robustness to channel estimation error under Rayleigh fading channel.
