  In this paper, we propose a learning-based block-wise planar channel
estimator (LBPCE) with high accuracy and low complexity to estimate the
time-varying frequency-selective channel of a multiple-input multiple-output
(MIMO) orthogonal frequency-division multiplexing (OFDM) system. First, we
establish a block-wise planar channel model (BPCM) to characterize the
correlation of the channel across subcarriers and OFDM symbols. Specifically,
adjacent subcarriers and OFDM symbols are divided into several sub-blocks, and
an affine function (i.e., a plane) with only three variables (namely, mean,
time-domain slope, and frequency-domain slope) is used to approximate the
channel in each sub-block, which significantly reduces the number of variables
to be determined in channel estimation. Second, we design a 3D dilated residual
convolutional network (3D-DRCN) that leverages the time-frequency-space-domain
correlations of the channel to further improve the channel estimates of each
user. Numerical results demonstrate that the proposed significantly outperforms
the state-of-the-art estimators and maintains a relatively low computational
complexity.
