Channel Estimation for Hybrid MIMO Systems With Array Model Errors and Beam Squint Effects

This paper proposes a channel estimation method for hybrid wideband multiple-input-multiple-output (MIMO) systems in high-frequency bands, including millimeter-wave (mmWave) and sub-terahertz (sub-THz), in the presence of beam squint effects and array errors arising from hardware impairments and environmental time fluctuations such as thermal effects and dynamic motion of the array. Although conventional channel estimation methods calibrate array errors through offline operation with large training pilots, the calibration errors remain due to time-varying array errors. Therefore, the proposed channel estimation method calibrates array errors online with small pilot overhead. In the proposed method, array response matrices are explicitly decomposed into a small number of physical parameters including path gains, angles and array errors, which are iteratively estimated by alternating optimization based on a maximum likelihood (ML) criterion. To enhance the convergence performance, we introduce a switching mechanism from an ongrid algorithm to an off-gird algorithm depending on the estimation accuracy of the array error during algorithmic iterations. Furthermore, we introduce an approximate mutual coupling model to reduce the number of parameters. The reduction of parameters not only lowers computational complexity but also mitigates overfitting to noisy observations. Numerical simulations demonstrate that the proposed method effectively works online even with small pilot overhead in the presence of array errors.

Joint Channel and Data Estimation for Multiuser Extremely Large-Scale MIMO Systems

This paper proposes a joint channel and data estimation (JCDE) algorithm for uplink multiuser extremely large-scale multiple-input-multiple-output (XL-MIMO) systems. The initial channel estimation is formulated as a sparse reconstruction problem based on the angle and distance sparsity under the near-field propagation condition. This problem is solved using non-orthogonal pilots through an efficient low complexity two-stage compressed sensing algorithm. Furthermore, the initial channel estimates are refined by employing a JCDE framework driven by both non-orthogonal pilots and estimated data. The JCDE problem is solved by sequential expectation propagation (EP) algorithms, where the channel and data are alternately updated in an iterative manner. In the channel estimation phase, integrating Bayesian inference with a model-based deterministic approach provides precise estimations to effectively exploit the near-field characteristics in the beam-domain. In the data estimation phase, a linear minimum mean square error (LMMSE)-based filter is designed at each sub-array to address the correlation due to energy leakage in the beam-domain arising from the near-field effects. Numerical simulations reveal that the proposed initial channel estimation and JCDE algorithm outperforms the state-ofthe-art approaches in terms of channel estimation, data detection, and computational complexity.