论文标题
MU-MIMO系统的低分辨率相位转换器的无监督学习杂种型融合体形成
Unsupervised Learning Based Hybrid Beamforming with Low-Resolution Phase Shifters for MU-MIMO Systems
论文作者
论文摘要
毫米波（MMWave）是第五代（5G）和超越通信的关键技术。已经提出了MMWave通信中的大规模天线系统的混合波束形成。由于硬件成本和功耗，现有基于无限分辨率相位变速器（PSS）的现有混合波束形成设计是不切实际的。在本文中，我们提出了一个基于学习的学习方案，以与多源多输入多输出（MU-MIMO）系统的低分辨率PSS共同设计模拟预编码器和组合。我们将模拟预编码器和组合设计问题转换为相分类问题，并提出了一个通用的神经网络体系结构，称为“相分类网络”（PCNET），能够生成各种PS分辨率的解决方案。仿真结果证明了所提出的方案的优势总和和复杂性性能，与最常用的低分辨率PS配置的最新混合杂种设计设计相比。
Millimeter wave (mmWave) is a key technology for fifth-generation (5G) and beyond communications. Hybrid beamforming has been proposed for large-scale antenna systems in mmWave communications. Existing hybrid beamforming designs based on infinite-resolution phase shifters (PSs) are impractical due to hardware cost and power consumption. In this paper, we propose an unsupervised-learning-based scheme to jointly design the analog precoder and combiner with low-resolution PSs for multiuser multiple-input multiple-output (MU-MIMO) systems. We transform the analog precoder and combiner design problem into a phase classification problem and propose a generic neural network architecture, termed the phase classification network (PCNet), capable of producing solutions of various PS resolutions. Simulation results demonstrate the superior sum-rate and complexity performance of the proposed scheme, as compared to state-of-the-art hybrid beamforming designs for the most commonly used low-resolution PS configurations.
