Recurrent Transformer-Based Near- and Far-Field THz Wideband Channel Estimation for UM-MIMO

TL;DR

This paper introduces a recurrent transformer model for accurate near- and far-field THz channel estimation in UM-MIMO systems, enhancing 6G network performance with robust, wideband capabilities.

Contribution

It proposes a single, trainable transformer block that generalizes across various channel conditions for hybrid-field channel estimation in UM-MIMO systems.

Findings

Achieves approximately 5 dB NMSE improvement in narrowband scenarios.

Achieves approximately 7.5 dB NMSE improvement in wideband scenarios.

Demonstrates effective generalization to channels with different scatterer distances and propagation paths.

Abstract

The integration of terahertz communications and ultra-massive multiple-input multiple-output (UM-MIMO) systems in 6G networks is motivated by their ability to enable unprecedented data rates, mitigate spectrum congestion, and enhance overall network performance. However, the enlarged antenna apertures and higher carrier frequencies in these systems increase the Rayleigh distance, causing users to span both the near-field and conventional far-field regions. Accurate spatial precoding thus requires exact channel estimation at the base station - a task made more challenging by the hybrid coexistence of near- and far-field effects and the limited number of digital chains available in hybrid beamforming architectures. In this paper, we propose a block recurrent transformer model to address this challenge. We demonstrate that a single transformer block equipped with state memory can be trained once and then iteratively applied for hybrid-field channel estimation. Furthermore, we train the model such that it generalizes to wireless channels with varying scatterer distances, different numbers of propagation paths, and wideband operation. Simulation results show that the proposed method achieves performance gains of approximately 5 dB and 7.5 dB in normalized mean squared error (NMSE) over state-of-the-art solutions in narrowband and wideband scenarios, respectively.