  To reap the promising benefits of massive multiple-input multiple-output
(MIMO) systems, accurate channel state information (CSI) is required through
channel estimation. However, due to the complicated wireless propagation
environment and large-scale antenna arrays, precise channel estimation for
massive MIMO systems is significantly challenging and costs an enormous
training overhead. Considerable time-frequency resources are consumed to
acquire sufficient accuracy of CSI, which thus severely degrades systems'
spectral and energy efficiencies. In this paper, we propose a
dual-attention-based channel estimation network (DACEN) to realize accurate
channel estimation via low-density pilots, by jointly learning the
spatial-temporal domain features of massive MIMO channels with the temporal
attention module and the spatial attention module. To further improve the
estimation accuracy, we propose a parameter-instance transfer learning approach
to transfer the channel knowledge learned from the high-density pilots
pre-acquired during the training dataset collection period. Experimental
results reveal that the proposed DACEN-based method achieves better channel
estimation performance than the existing methods under various pilot-density
settings and signal-to-noise ratios. Additionally, with the proposed
parameter-instance transfer learning approach, the DACEN-based method achieves
additional performance gain, thereby further demonstrating the effectiveness
and superiority of the proposed method.
