Due to a high spatial angle resolution and low circuit cost of massive hybrid analog and digital (HAD) multiple-input multiple-output (MIMO), it is viewed as a valuable green communication technology for future wireless networks. Combining a massive HAD-MIMO with direction of arrival (DOA) will provide a high-precision even ultra-high-precision DOA measurement performance approaching the fully-digital (FD) MIMO. However, phase ambiguity is a challenge issue for a massive HAD-MIMO DOA estimation. In this paper, we review three aspects: detection, estimation, and Cramer-Rao lower bound (CRLB) with low-resolution ADCs at receiver. First, a multi-layer-neural-network (MLNN) detector is proposed to infer the existence of passive emitters. Then, a two-layer HAD (TLHAD) MIMO structure is proposed to eliminate phase ambiguity using only one-snapshot. Simulation results show that the proposed MLNN detector is much better than both the existing generalized likelihood ratio test (GRLT) and the ratio of maximum eigen-value (Max-EV) to minimum eigen-value (R-MaxEV-MinEV) in terms of detection probability. Additionally, the proposed TLHAD structure can achieve the corresponding CRLB using single snapshot.

翻译：由于大规模混合模拟与数字（HAD）多输入多输出（MIMO）技术具有高空间角度分辨率和低电路成本，它被视为未来无线网络中一项有价值的绿色通信技术。将大规模HAD-MIMO与到达方向（DOA）相结合，能够提供接近全数字（FD）MIMO的高精度甚至超高精度DOA测量性能。然而，相位模糊是大规模HAD-MIMO DOA估计中的一个挑战性问题。本文回顾了三个方面的内容：检测、估计以及接收端采用低分辨率ADC时的克拉美-罗下界（CRLB）。首先，提出了一种多层神经网络（MLNN）检测器来推断无源发射源的存在。然后，提出了一种双层HAD（TLHAD）MIMO结构，仅利用单次快照即可消除相位模糊。仿真结果表明，所提出的MLNN检测器在检测概率方面明显优于现有的广义似然比检验（GLRT）以及最大特征值与最小特征值之比（R-MaxEV-MinEV）。此外，所提出的TLHAD结构能够利用单次快照达到相应的CRLB。