Hybrid Near- and Far-Field THz UM-MIMO Channel Estimation: A Sparsifying Matrix Learning-Aided Bayesian Approach

Yuanjian Li*, A. S. Madhukumar

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Channel estimation (CE) is a critical challenge in harnessing the potential of Terahertz (THz) ultra-massive multiple-input multiple-output (UM-MIMO) systems. Sparsity-exploiting compressed sensing (CS)-aided CE (CSCE) can enhance THz UM-MIMO CE performance with affordable pilot overhead. However, the near-field propagation region becomes significant in THz UM-MIMO networks due to the large array aperture and high carrier frequency, leading to a more profound coexistence of near- and far-field radiation patterns. This hybrid-field propagation characteristic renders existing CSCE frameworks ineffective due to the lack of an appropriate sparsifying matrix. In this work, we investigate the uplink THz UM-MIMO CE problem, by developing a practical THz UM-MIMO channel model that incorporates near- and far-field paths, molecular absorption, and reflection attenuation. We propose a dictionary learning (DL)-aided Bayesian THz CSCE solution to achieve accurate, robust and pilot-efficient CE, even in ill-posed scenarios. Specifically, we tailor a batch-delayed online DL (BD-ODL) algorithm to generate an appropriate dictionary for the hybrid-field THz UM-MIMO channel model. Furthermore, we propose a Bayesian learning (BL)-enabled CSCE framework to leverage THz sparsity and utilize the learnt dictionary. To establish a lower bound for the mean squared error (MSE), we derive the Bayesian Cramér-Rao bound (BCRB). We also conduct a complexity analysis to quantify the required computational resources. Numerical results show a significant improvement in normalized MSE (NMSE) performance compared to conventional CE and CSCE baselines, and demonstrate rapid convergence.

Original languageEnglish
Pages (from-to)1881-1897
Number of pages17
JournalIEEE Transactions on Wireless Communications
Volume24
Issue number3
DOIs
Publication statusPublished - 2025
Externally publishedYes

Keywords

  • channel estimation
  • compressed sensing
  • dictionary learning
  • Terahertz communications
  • ultra-massive multiple-input multiple-output systems

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