• 1. AI-empowered Wireless Communications

1. 1. Channel estimation for massive MIMO systems with low density pilots

• • Binggui Zhou, Xi Yang, Shaodan Ma, Feifei Gao and Guanghua Yang, “Pay Less But Get More: A Dual-Attention-based Channel Estimation Network for Massive MIMO Systems with Low-Density Pilots”, accepted by IEEE Transactions on Wireless Communications

A dual-attention-based channel estimation network (DACEN) is proposed 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, a parameter-instance transfer learning approach is also proposed 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.