Terahertz ultra-massive multiple-input multiple-output (THz UM-MIMO) is envisioned as one of the key enablers of 6G wireless systems. Due to the joint effect of its array aperture and small wavelength, the near-field region of THz UM-MIMO is greatly enlarged. The high-dimensional channel of such systems thus consists of a stochastic mixture of far and near fields, which renders channel estimation extremely challenging. Previous works based on uni-field assumptions cannot capture the hybrid far- and near-field features, thus suffering significant performance loss. This motivates us to consider hybrid-field channel estimation. We draw inspirations from fixed point theory to develop an efficient deep learning based channel estimator with adaptive complexity and linear convergence guarantee. Built upon classic orthogonal approximate message passing, we transform each iteration into a contractive mapping, comprising a closed-form linear estimator and a neural network based non-linear estimator. A major algorithmic innovation involves applying fixed point iteration to compute the channel estimate while modeling neural networks with arbitrary depth and adapting to the hybrid-field channel conditions. Simulation results verify our theoretical analysis and show significant performance gains over state-of-the-art approaches in the estimation accuracy and convergence rate.

翻译：太赫兹超大规模多输入多输出（THz UM-MIMO）被视为6G无线系统的关键使能技术之一。受阵列孔径与短波长联合效应影响，THz UM-MIMO的近场区域被极大扩展。此类系统的高维信道由远近场随机混合构成，导致信道估计面临严峻挑战。现有基于单一场假设的方法无法捕获混合远近场特征，因而出现显著性能损失。这促使我们研究混合场信道估计问题。受不动点理论启发，我们开发了一种兼具自适应复杂度与线性收敛保证的深度学习信道估计器。基于经典正交近似消息传递框架，我们将每次迭代转化为包含闭式线性估计器与神经网络非线性估计器的压缩映射。核心算法创新在于：通过不动点迭代计算信道估计值，同时支持任意深度的神经网络建模并能自适应混合场信道条件。仿真结果验证了理论分析，并在估计精度与收敛速度上展现出相较于现有最优方法的显著性能提升。