  Terahertz (THz) communications is considered as one of key solutions to
support extremely high data demand in 6G. One main difficulty of the THz
communication is the severe signal attenuation caused by the foliage loss,
oxygen/atmospheric absorption, body and hand losses. To compensate for the
severe path loss, multiple-input-multiple-output (MIMO) antenna array-based
beamforming has been widely used. Since the beams should be aligned with the
signal propagation path to achieve the maximum beamforming gain, acquisition of
accurate channel knowledge, i.e., channel estimation, is of great importance.
An aim of this paper is to propose a new type of deep learning (DL)-based
parametric channel estimation technique. In our work, DL figures out the
mapping function between the received pilot signal and the sparse channel
parameters characterizing the spherical domain channel. By exploiting the long
short-term memory (LSTM), we can efficiently extract the temporally correlated
features of sparse channel parameters and thus make an accurate estimation with
relatively small pilot overhead. From the numerical experiments, we show that
the proposed scheme is effective in estimating the near-field THz MIMO channel
in THz downlink environments.
