Wideband Hybrid-Field THz UM-MIMO Channel Estimation: A Dual-Attention-Aided Deep-Unfolded Bayesian Learning Approach

To efficiently implement Terahertz (THz) communications in the 6G era, ultra-massive multiple-input multiple-output (UM-MIMO) technique is considered essential. However, effective wideband THz UM-MIMO transmissions necessitate low-cost yet accurate channel estimation (CE) methods. In this article, we investigate the wideband THz UM-MIMO CE problem under hybrid near- and far-field propagation, molecular absorption, and multi-path reflection. The CE problem is reformulated into a compressed sensing (CS)-aided counterpart (CSCE), exploiting the inherent sparsity of THz UM-MIMO channels to reduce pilot overhead. Our key contributions are: 1) after analyzing the inefficiency of conventional Bayesian learning (BL)-based CSCE frameworks in solving this CE task, we propose a deep unfolding (DU)-aided BL (DUBL) CE algorithm, in which the unfolded expectation-maximization (EM) iteration is implemented through a carefully tailored deep neural network (DNN) architecture; 2) we design a staged offline training procedure equipped with a dedicated loss function to ensure efficient DUBL training; and 3) we conduct a detailed complexity analysis that explicitly quantifies the computational cost of each unrolled layer, thereby characterizing the online inference overhead of the proposed DUBL method. Simulation results demonstrate that the DUBL solution offers substantial THz UM-MIMO CE gains over representative baselines, while complexity comparison highlights its enhanced real-time inference.