Pay Less But Get More: A Dual-Attention-based Channel Estimation Network for Massive MIMO Systems with Low-Density Pilots

• URL: http://arxiv.org/abs/2303.00986v2
• Date: Thu, 9 Nov 2023 11:01:19 GMT
• Title: Pay Less But Get More: A Dual-Attention-based Channel Estimation Network for Massive MIMO Systems with Low-Density Pilots
• Authors: Binggui Zhou, Xi Yang, Shaodan Ma, Feifei Gao, and Guanghua Yang
• Abstract summary: We propose a dual-attention-based channel estimation network (DACEN) to realize accurate channel estimation via low-density pilots. Experimental results reveal that the proposed DACEN-based method achieves better channel estimation performance than the existing methods.
• Score: 41.213515826100696
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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