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.

翻译：为充分发挥大规模多输入多输出（MIMO）系统的优势，需要通过信道估计获取精确的信道状态信息（CSI）。然而，由于复杂的无线传播环境和大规模天线阵列，大规模MIMO系统的精确信道估计面临显著挑战且需要巨大的训练开销。为获得足够精确的CSI，需消耗大量时频资源，从而严重降低系统的频谱效率和能量效率。本文提出一种基于双重注意力机制的信道估计网络（DACEN），通过时域注意力模块和空域注意力模块联合学习大规模MIMO信道的时空域特征，实现低导频密度下的精确信道估计。为进一步提升估计精度，我们提出参数-实例迁移学习方法，将训练数据采集阶段预先获取的高密度导频中学习到的信道知识进行迁移。实验结果表明，在不同导频密度设置和信噪比条件下，基于DACEN的方法相比现有方法取得了更优的信道估计性能。此外，通过所提出的参数-实例迁移学习方法，基于DACEN的方法获得了额外性能增益，进一步证明了所提方法的有效性和优越性。