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

TL;DR

This paper introduces a joint channel and data estimation algorithm for uplink XL-MIMO systems, leveraging near-field sparsity and iterative Bayesian inference to improve accuracy and efficiency.

Contribution

It proposes a novel JCDE framework combining compressed sensing and expectation propagation for near-field XL-MIMO systems, enhancing estimation accuracy over existing methods.

Findings

Outperforms state-of-the-art in channel estimation and data detection

Reduces computational complexity compared to previous approaches

Effectively exploits near-field characteristics in the beam domain

Abstract

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-of-the-art approaches in terms of channel estimation, data detection, and computational complexity.