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:在宽带宽波(mmWave)大规模多输出多输出系统(MIMO)中,由于混合的模拟-数字结构,频道估算具有挑战性,这种结构压缩了接收的试点信号,使频道估算成为压缩感应问题;然而,现有的高性能 CS 算法通常具有高度复杂性;另一方面,由巨大的带宽和大型天线造成的波束斜形效应将恶化估计性能;在本文中,首先采用依赖频率的角字典来弥补波音之光。随后,基于预期的Mexim化(EM)的稀有巴耶斯人学习算法在两个方面得到了加强,即每次迭代的E级方法是通过近似信息传递(AMP)来降低复杂性,而M级则由一个深神经网络(DNN)来实现,以提高性能。在模拟中,拟议的AM-SBL的频谱频道测算仪以低复杂性取得令人满意的性能。