Two-Dimensional Ice Filling Based Channel Estimation in Densifying MIMO Systems

Densifying multiple-input multiple-output (MIMO) has attracted much attention in recent years. The strong correlations among densifying antennas provide sufficient prior knowledge about channel state information (CSI), which inspires the careful design of observation matrices (e.g., transmit precoders and receive combiners) to boost channel estimation performance. To achieve this, this work proposes to jointly design the combiners and precoders by maximizing the mutual information between the received pilots and densifying MIMO channels. Particularly, a two-dimensional ice-filling (2DIF) algorithm is proposed, which is motivated by the fact that the eigenspace of MIMO channel covariance can be decoupled into two sub-eigenspaces. By properly setting the precoder and the combiner as the eigenvectors from these two sub-eigenspaces, the 2DIF promises to generate nearoptimal observation matrices for channel estimation. Simulation results demonstrate that, the proposed 2DIF method outperforms the state-of-the-art schemes in channel estimation accuracy.