论文标题
深度学习辅助延迟耐药的零孔预码
Deep Learning-Aided Delay-Tolerant Zero-Forcing Precoding in Cell-Free Massive MIMO
论文作者
论文摘要
在无细胞的大规模多输入多输出（CFMMIMO）的背景下，零孔的预编码（ZFP）在光谱效率方面表现出色。但是，由于领先和处理延迟，它因通道老化而受苦。在本文中，我们提出了一个强大的方案，提出了耐延迟的零式预编码（DT-ZFP），该方案利用了深度学习辅助通道预测以减轻过时的渠道状态信息（CSI）的影响。由特定于用户的预测模块组成的预测变量是专门为这种多用户方案设计的。利用预测范围所带来的自由度，可以通过Fronthaul网络传递CSI和预编码的数据，并且可以平行将用户数据和飞行员传输到空气界面上。因此，DT-ZFP不仅有效地打击了通道老化，而且还避免了CFMMIMO中规范ZFP的效率低下的停止和等待机制。
In the context of cell-free massive multi-input multi-output (CFmMIMO), zero-forcing precoding (ZFP) is superior in terms of spectral efficiency. However, it suffers from channel aging owing to fronthaul and processing delays. In this paper, we propose a robust scheme coined delay-tolerant zero-forcing precoding (DT-ZFP), which exploits deep learning-aided channel prediction to alleviate the effect of outdated channel state information (CSI). A predictor consisting of a bank of user-specific predictive modules is specifically designed for such a multi-user scenario. Leveraging the degree of freedom brought by the prediction horizon, the delivery of CSI and precoded data through a fronthaul network and the transmission of user data and pilots over an air interface can be parallelized. Therefore, DT-ZFP not only effectively combats channel aging but also avoids the inefficient Stop-and-Wait mechanism of the canonical ZFP in CFmMIMO.
