Terahertz ultra-massive MIMO (THz UM-MIMO) is envisioned as one of the key enablers of 6G wireless networks, for which channel estimation is highly challenging. Traditional analytical estimation methods are no longer effective, as the enlarged array aperture and the small wavelength result in a mixture of far-field and near-field paths, constituting a hybrid-field channel. Deep learning (DL)-based methods, despite the competitive performance, generally lack theoretical guarantees and scale poorly with the size of the array. In this paper, we propose a general DL framework for THz UM-MIMO channel estimation, which leverages existing iterative channel estimators and is with provable guarantees. Each iteration is implemented by a fixed point network (FPN), consisting of a closed-form linear estimator and a DL-based non-linear estimator. The proposed method perfectly matches the THz UM-MIMO channel estimation due to several unique advantages. First, the complexity is low and adaptive. It enjoys provable linear convergence with a low per-iteration cost and monotonically increasing accuracy, which enables an adaptive accuracy-complexity tradeoff. Second, it is robust to practical distribution shifts and can directly generalize to a variety of heavily out-of-distribution scenarios with almost no performance loss, which is suitable for the complicated THz channel conditions. For practical usage, the proposed framework is further extended to wideband THz UM-MIMO systems with beam squint effect. Theoretical analysis and extensive simulation results are provided to illustrate the advantages over the state-of-the-art methods in estimation accuracy, convergence rate, complexity, and robustness.

翻译：太赫兹超大规模MIMO（THz UM-MIMO）被视为6G无线网络的关键支撑技术之一，其信道估计极具挑战性。由于阵列孔径增大和波长减小导致远场与近场路径混合，构成混合场信道，传统解析估计方法不再有效。基于深度学习（DL）的方法虽具有竞争力，但普遍缺乏理论保证且随阵列规模扩展性差。本文提出面向THz UM-MIMO信道估计的通用深度学习框架，该框架利用现有迭代信道估计器并具有可证明的理论保证。每次迭代由定点网络（FPN）实现，包含闭式线性估计器和基于DL的非线性估计器。所提方法凭借多项独特优势完美适配THz UM-MIMO信道估计：首先，复杂度低且自适应，具有可证明的线性收敛性、低单次迭代成本和单调递增的估计精度，可实现精度-复杂度的自适应折中；其次，对实际分布偏移具有鲁棒性，在多种严重分布外场景中几乎无性能损失直接泛化，适用于复杂的THz信道条件。为实用化，该框架进一步扩展到存在波束斜视效应的宽带THz UM-MIMO系统。理论分析与大量仿真结果表明，该方法在估计精度、收敛速率、复杂度和鲁棒性方面均优于现有最优方法。