Compressed Channel Estimation for Near-Field XL-MIMO Using Triple Parametric Decomposition
Xufeng Guo, Yuanbin Chen, Ying Wang
Abstract
With the presence of the conventional angular-domain representation, the energy leakage effect is evident for the near-field extremely large-scale multiple-input-multiple-output (XL-MIMO) channel, which further leads to ambiguous channel power detection, significantly eroding channel estimation performance. To tackle this challenge, this paper proposes a novel Triple Parametric Decomposition (TPD) framework to facilitate compressed channel estimation in the XL-MIMO system. Specifically, we examine the case of a uniform planar array (UPA) with the geometry parameters involving a pair of azimuth-elevation angles and distances in an attempt to achieve independent decomposition by constructing their own covariance matrices. Then, by utilizing the on- and off-grid techniques, we obtain the sparse recovery of angles and distances information in the absence of energy leakage effect at reduced gridding complexity. Simulation results demonstrate that our proposed TPD achieves significant performance improvement over the state-of-the-art methods.