论文标题
用于未来无线网络的机器学习辅助大型混合模拟和数字MIMO DOA估计
Machine-learning-aided Massive Hybrid Analog and Digital MIMO DOA Estimation for Future Wireless Networks
论文作者
论文摘要
由于高空间角度分辨率和大型混合模拟和数字（具有）多输入多输出（MIMO）的低回路成本，因此它被视为未来无线网络的宝贵绿色通信技术。将大量的had-mimo与到达方向（DOA）结合在一起，即使是超高精确的DOA测量性能，也将提供高精度，即接近完全数字（FD）MIMO。但是，对于大规模的HADIMO DOA估计，相位歧义是一个挑战问题。在本文中，我们回顾了三个方面：检测，估计和CRAMER-RAO下限（CRLB），在接收器处有低分辨率ADC。首先，提出了多层神经网络（MLNN）检测器来推断被动发射器的存在。然后，提出了一个两层具有（TLHAD）的MIMO结构，以消除仅使用一个snapshot的相位歧义。仿真结果表明，提出的MLNN检测器比现有的广义似然比测试（GRLT）和最大本本值（MAX-EV）与最小本本特征值（R-Maxev-Minev）的比率要好得多。另外，提出的TLHAD结构可以使用单个快照实现相应的CRLB。
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.
