Deep Learning-Aided Delay-Tolerant Zero-Forcing Precoding in Cell-Free
Massive MIMO
Abstract
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learning-aided channel prediction to alleviate the effect of outdated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.