  A major obstacle for widespread deployment of frequency division duplex
(FDD)-based Massive multiple-input multiple-output (MIMO) communications is the
large signaling overhead for reporting full downlink (DL) channel state
information (CSI) back to the basestation (BS), in order to enable closed-loop
precoding. We completely remove this overhead by a deep-learning based channel
extrapolation (or "prediction") approach and demonstrate that a neural network
(NN) at the BS can infer the DL CSI centered around a frequency $f_\text{DL}$
by solely observing uplink (UL) CSI on a different, yet adjacent frequency band
around $f_\text{UL}$; no more pilot/reporting overhead is needed than with a
genuine time division duplex (TDD)-based system. The rationale is that
scatterers and the large-scale propagation environment are sufficiently similar
to allow a NN to learn about the physical connections and constraints between
two neighboring frequency bands, and thus provide a well-operating system even
when classic extrapolation methods, like the Wiener filter (used as a baseline
for comparison throughout) fails. We study its performance for various
state-of-the-art Massive MIMO channel models, and, even more so, evaluate the
scheme using actual Massive MIMO channel measurements, rendering it to be
practically feasible at negligible loss in spectral efficiency when compared to
a genuine TDD-based system.
