论文标题
基于深度学习的DOA估计杂交大型MIMO接收带有重叠子阵列的阵列
Deep Learning Based DOA Estimation for Hybrid Massive MIMO Receive Array with Overlapped Subarrays
论文作者
论文摘要
为了提高到达方向（DOA）估计的准确性，提出了一种基于深度学习（DL）的方法称为CDAE-DNN，用于混合模拟和数字（HAT）大量MIMO接收阵列，并在本文中具有重叠的子阵列（OSA）结构。在建议的方法中，样品协方差矩阵（SCM）首先输入到卷积Denoise自动编码器（CDAE）以删除近似误差，然后将CDAE的输出导入到完全连接的（FC）网络中以获得估计结果。基于模拟结果，所提出的CDAE-DNN比传统音乐算法和基于CNN的方法具有巨大的性能优势，尤其是在信号噪声比（SNR）低的情况下和低快照数字的情况下。与未经封闭的子阵列（NOSA）体系结构相比，OSA体系结构还显示出显着提高估计精度。此外，提出了Had-Osa架构的Cramer-Rao下限（CRLB）。
To improve the accuracy of direction-of-arrival (DOA) estimation, a deep learning (DL)-based method called CDAE-DNN is proposed for hybrid analog and digital (HAD) massive MIMO receive array with overlapped subarray (OSA) architecture in this paper. In the proposed method, the sample covariance matrix (SCM) is first input to a convolution denoise autoencoder (CDAE) to remove the approximation error, then the output of CDAE is imported to a fully-connected (FC) network to get the estimation result. Based on the simulation results, the proposed CDAE-DNN has great performance advantages over traditional MUSIC algorithm and CNN-based method, especially in the situations with low signal to noise ratio (SNR) and low snapshot numbers. And the OSA architecture has also been shown to significantly improve the estimation accuracy compared to non-overlapped subarray (NOSA) architecture. In addition, the Cramer-Rao lower bound (CRLB) for the HAD-OSA architecture is presented.
