  To enhance flexibility and facilitate resource cooperation, a novel
fully-decoupled radio access network (FD-RAN) architecture is proposed for 6G.
However, the decoupling of uplink (UL) and downlink (DL) in FD-RAN makes the
existing feedback mechanism ineffective. To this end, we propose an end-to-end
data-driven MIMO solution without the conventional channel feedback procedure.
Data-driven MIMO can alleviate the drawbacks of feedback including overheads
and delay, and can provide customized precoding design for different BSs based
on their historical channel data. It essentially learns a mapping from
geolocation to MIMO transmission parameters. We first present a codebook-based
approach, which selects transmission parameters from the statistics of discrete
channel state information (CSI) values and utilizes integer interpolation for
spatial inference. We further present a non-codebook-based approach, which 1)
derives the optimal precoder from the singular value decomposition (SVD) of the
channel; 2) utilizes variational autoencoder (VAE) to select the representative
precoder from the latent Gaussian representations; and 3) exploits Gaussian
process regression (GPR) to predict unknown precoders in the space domain.
Extensive simulations are performed on a link-level 5G simulator using
realistic ray-tracing channel data. The results demonstrate the effectiveness
of data-driven MIMO, showcasing its potential for application in FD-RAN and 6G.
