This paper proposes a deep learning approach to channel sensing and downlink hybrid analog and digital beamforming for massive multiple-input multiple-output systems with a limited number of radio-frequency chains operating in the time-division duplex mode at millimeter frequency. The conventional downlink precoding design hinges on the two-step process of first estimating the high-dimensional channel based on the uplink pilots received through the channel sensing matrices, then designing the precoding matrices based on the estimated channel. This two-step process is, however, not necessarily optimal, especially when the pilot length is short. This paper shows that by designing the analog sensing and the downlink precoding matrices directly from the received pilots without the intermediate channel estimation step, the overall system performance can be significantly improved. Specifically, we propose a channel sensing and hybrid precoding methodology that divides the pilot phase into an analog and a digital training phase. A deep neural network is utilized in the first phase to design the uplink channel sensing and the downlink analog beamformer. Subsequently, we fix the analog beamformers and design the digital precoder based on the equivalent low-dimensional channel. A key feature of the proposed deep learning architecture is that it decomposes into parallel independent single-user DNNs so that the overall design is generalizable to systems with an arbitrary number of users. Numerical comparisons reveal that the proposed methodology requires significantly less training overhead than the channel recovery based counterparts, and can approach the performance of systems with full channel state information with relatively few pilots.

中文翻译：

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用于TDD大规模MIMO系统的信道感知和混合预编码的深度学习方法

本文提出了一种深度学习方法，用于在时分双工模式下以毫米波频率工作的有限数量的射频链的大规模多输入多输出系统的信道感测以及下行链路混合模拟和数字波束成形。常规的下行链路预编码设计取决于两步过程：首先基于通过信道感测矩阵接收的上行链路导频来估计高维信道，然后根据估计的信道来设计预编码矩阵。但是，此两步过程不一定是最佳的，尤其是在导频长度较短时。本文显示，通过直接从接收的导频设计模拟感测和下行链路预编码矩阵，而无需中间信道估计步骤，整体系统性能可以大大提高。具体而言，我们提出了一种信道检测和混合预编码方法，该方法将导频阶段分为模拟阶段和数字训练阶段。在第一阶段中，使用深度神经网络来设计上行链路信道感测和下行链路模拟波束形成器。随后，我们修复了模拟波束形成器，并基于等效的低维信道设计了数字预编码器。所提出的深度学习架构的一个关键特征是，它分解为并行的独立单用户DNN，因此总体设计可推广到具有任意数量用户的系统。数值比较表明，与基于信道恢复的对应方法相比，所提出的方法所需的训练开销明显更少，