  Massive multiple-input multiple-output (MIMO) is believed to deliver
unrepresented spectral efficiency gains for 5G and beyond. However, a practical
challenge arises during its commercial deployment, which is known as the
``curse of mobility''. The performance of massive MIMO drops alarmingly when
the velocity level of user increases. In this paper, we tackle the problem in
frequency division duplex (FDD) massive MIMO with a novel Channel State
Information (CSI) acquisition framework. A joint angle-delay-Doppler (JADD)
wideband precoder is proposed for channel training. Our idea consists in the
exploitation of the partial channel reciprocity of FDD and the
angle-delay-Doppler channel structure. More precisely, the base station (BS)
estimates the angle-delay-Doppler information of the UL channel based on UL
pilots using Matrix Pencil (MP) method. It then computes the wideband JADD
precoders according to the extracted parameters. Afterwards, the user estimates
and feeds back some scalar coefficients for the BS to reconstruct the predicted
DL channel. Asymptotic analysis shows that the CSI prediction error converges
to zero when the number of BS antennas and the bandwidth increases. Numerical
results with industrial channel model demonstrate that our framework can well
adapt to high speed (350 km/h), large CSI delay (10 ms) and channel sample
noise.
