In wideband millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, channel estimation is challenging due to the hybrid analog-digital architecture, which compresses the received pilot signal and makes channel estimation a compressive sensing (CS) problem. However, existing high-performance CS algorithms usually suffer from high complexity. On the other hand, the beam squint effect caused by huge bandwidth and massive antennas will deteriorate estimation performance. In this paper, frequency-dependent angular dictionaries are first adopted to compensate for beam squint. Then, the expectation-maximization (EM)-based sparse Bayesian learning (SBL) algorithm is enhanced in two aspects, where the E-step in each iteration is implemented by approximate message passing (AMP) to reduce complexity while the M-step is realized by a deep neural network (DNN) to improve performance. In simulation, the proposed AMP-SBL unfolding-based channel estimator achieves satisfactory performance with low complexity.

翻译：在宽带毫米波（mmWave）大规模多输入多输出（MIMO）系统中，由于混合模数架构压缩了接收导频信号，信道估计具有挑战性，这使得信道估计成为一个压缩感知（CS）问题。然而，现有的高性能CS算法通常复杂度较高。另一方面，由巨大带宽和大量天线引起的波束斜视效应会恶化估计性能。本文首先采用频率相关的角度字典来补偿波束斜视。然后，从两个方面改进了基于期望最大化（EM）的稀疏贝叶斯学习（SBL）算法，其中每次迭代的E步通过近似消息传递（AMP）实现以降低复杂度，而M步通过深度神经网络（DNN）实现以提高性能。仿真实验中，所提出的基于AMP-SBL展开的信道估计器以低复杂度实现了满意的性能。