  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prediction to alleviate the effect of outdated channel state information (CSI).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.
