  Estimating downlink (DL) channel state information (CSI) in frequency
division duplex (FDD) massive multi-input multi-output (MIMO) systems generally
requires downlink pilots and feedback overheads. Accordingly, this paper
investigates the feasibility of zero-feedback FDD massive MIMO systems based on
channel extrapolation. We use the high-resolution parameter estimation (HRPE),
specifically the space-alternating generalized expectation-maximization (SAGE)
algorithm, to extrapolate the DL CSI based on the extracted parameters of
multipath components in the uplink channel. We apply the HRPE to two different
channel models: the vector spatial signature (VSS) model and the direction of
arrival (DOA) model. We verify these methods through real-world channel data
acquired from channel measurement campaigns with two different types of channel
sounders: a) a switched array-based, real-time, time-domain, outdoors setup at
3.5 GHz, and b) a virtual array-based, high-accuracy, frequency-domain, indoors
setup at 2.4 and 5-7 GHz. The performance metrics of the extrapolated channels
that we evaluate include the mean squared error, beamforming efficiency, and
spectral efficiency in multiuser MIMO scenarios. The results show that the
HRPE-based channel extrapolation performs best under the simple VSS model,
which does not require array calibration, and if the BS is in an open outdoor
environment having line-of-sight (LOS) paths to well-separated users.
