  Although it is often used in the orthogonal frequency division multiplexing
(OFDM) systems, application of massive multiple-input multiple-output (MIMO)
over the orthogonal time frequency space (OTFS) modulation could suffer from
enormous training overhead in high mobility scenarios. In this paper, we
propose one uplink-aided high mobility downlink channel estimation scheme for
the massive MIMO-OTFS networks. Specifically, we firstly formulate the time
domain massive MIMO-OTFS signal model along the uplink and adopt the
expectation maximization based variational Bayesian (EM-VB) framework to
recover the uplink channel parameters including the angle, the delay, the
Doppler frequency, and the channel gain for each physical scattering path.
Correspondingly, with the help of the fast Bayesian inference, one low complex
approach is constructed to overcome the bottleneck of the EM-VB. Then, we fully
exploit the angle, delay and Doppler reciprocity between the uplink and the
downlink and reconstruct the angles, the delays, and the Doppler frequencies
for the downlink massive channels at the base station. Furthermore, we examine
the downlink massive MIMO channel estimation over the delay-Doppler-angle
domain. The channel dispersion of the OTFS over the delay-Doppler domain is
carefully analyzed. Various numerical examples are presented to confirm the
validity and robustness of the proposed scheme.
