论文标题
在TDD大规模MIMO OFDM系统中进行通道传感和混合预编码的深度学习
Deep Learning for Channel Sensing and Hybrid Precoding in TDD Massive MIMO OFDM Systems
论文作者
论文摘要
本文提出了一种深度学习方法，以通道传感和下行链路混合波束成形，以在时间划分双面模式下运行的大量多输入多输出系统，并采用单载波或多载波传输。常规的预码设计涉及首先估算高维通道的两步过程，然后根据这种估计设计预编码器。但是，这个两步过程不一定是最佳的。本文表明，通过使用学习方法来设计模拟传感和直接从接收的飞行员那里的混合下行链路预编码器，而无需中等高维通道估计，可以显着改善整体系统性能。但是，训练神经网络以同时设计模拟和数字预码器，这是困难的。此外，这种方法无法推广到具有不同用户数量的系统。在本文中，我们开发了一种简化且可推广的方法，该方法使用深层神经网络学习上行链路传感矩阵和下行链路模拟预码器，该网络以每个用户的基础分解，然后根据估计的低维等效通道设计数字预码器。数值比较表明，所提出的方法会导致训练开销大大降低，并导致构建概括为各种系统设置。
This paper proposes a deep learning approach to channel sensing and downlink hybrid beamforming for massive multiple-input multiple-output systems operating in the time division duplex mode and employing either single-carrier or multicarrier transmission. The conventional precoding design involves a two-step process of first estimating the high-dimensional channel, then designing the precoders based on such estimate. This two-step process is, however, not necessarily optimal. This paper shows that by using a learning approach to design the analog sensing and the hybrid downlink precoders directly from the received pilots without the intermediate high-dimensional channel estimation, the overall system performance can be significantly improved. Training a neural network to design the analog and digital precoders simultaneously is, however, difficult. Further, such an approach is not generalizable to systems with different number of users. In this paper, we develop a simplified and generalizable approach that learns the uplink sensing matrix and downlink analog precoder using a deep neural network that decomposes on a per-user basis, then designs the digital precoder based on the estimated low-dimensional equivalent channel. Numerical comparisons show that the proposed methodology results in significantly less training overhead and leads to an architecture that generalizes to various system settings.
