  This paper proposes and analyzes novel deep learning methods for downlink
(DL) single-user multiple-input multiple-output (SU-MIMO) and multi-user MIMO
(MU-MIMO) systems operating in time division duplex (TDD) mode. A motivating
application is the 6G upper midbands (7-24 GHz), where the base station (BS)
antenna arrays are large, user equipment (UE) array sizes are moderate, and
theoretically optimal approaches are practically infeasible for several
reasons. To deal with uplink (UL) pilot overhead and low signal power issues,
we introduce the channel-adaptive pilot, as part of an analog channel state
information feedback mechanism. Deep neural network (DNN)-generated pilots are
used to linearly transform the UL channel matrix into lower-dimensional latent
vectors. Meanwhile, the BS employs a second DNN that processes the received UL
pilots to directly generate near-optimal DL precoders. The training is
end-to-end which exploits synergies between the two DNNs. For MU-MIMO
precoding, we propose a DNN structure inspired by theoretically optimum linear
precoding. The proposed methods are evaluated against genie-aided upper bounds
and conventional approaches, using realistic upper midband datasets. Numerical
results demonstrate the potential of our approach to achieve significantly
increased sum-rate, particularly at moderate to high signal-to-noise ratio
(SNR) and when UL pilot overhead is constrained.
