论文标题
基于学习的Max-Min Fair Hybrid预编码MMWave多播
Learning-based Max-Min Fair Hybrid Precoding for mmWave Multicasting
论文作者
论文摘要
本文研究了混合传输预编码器和模拟的联合设计接收组合器，用于单组多播在毫米波系统中。我们提出了LB-GDM，这是一种基于低复杂性学习的方法，该方法利用动量和优化的设计来利用梯度下降（i）混合发射器的数字和模拟组成部分以及（ii）每个接收器的模拟组合者。此外，我们还扩展了我们提出的设计完全数字预编码器的方法。我们通过数值评估表明，与基于半决赛放松的竞争设计相比，在混合动力或数字预码器中实现LB-GDM的最高表现。具体而言，就最小信噪比而言，我们报告的改善显着改善，完全数字和混合预言器的增长分别为105％和101％。
This paper investigates the joint design of hybrid transmit precoder and analog receive combiners for single-group multicasting in millimeter-wave systems. We propose LB-GDM, a low-complexity learning-based approach that leverages gradient descent with momentum and alternating optimization to design (i) the digital and analog constituents of a hybrid transmitter and (ii) the analog combiners of each receiver. In addition, we also extend our proposed approach to design fully-digital precoders. We show through numerical evaluation that, implementing LB-GDM in either hybrid or digital precoders attain superlative performance compared to competing designs based on semidefinite relaxation. Specifically, in terms of minimum signal-to-noise ratio, we report a remarkable improvement with gains of up to 105% and 101% for the fully-digital and hybrid precoders, respectively.
