一种基于深度学习的环形阵列混合波束成型大规模MIMO系统DOA估计算法

A Deep-Learning-Based DOA Estimation Method for Hybrid Beamforming Massive MIMO Systems with Uniform Circular Array

研究了采用混合波束成型结构和环形阵列的大规模多输入多输出系统中的波达方向(direction of arrival,DOA)估计,提出了一种基于深度学习的低复杂度的DOA估计算法。所提算法首先离线训练一个深度神经网络,然后再利用该网络进行在线DOA估计。在线估计中,算法首先将接收信号送入网络,然后根据网络给出的初始角度估计产生一个候选角度集合,最后选择集合中最大似然估计结果最优的角度作为最终DOA估计值。仿真结果显示,与传统的最大似然方法相比,提出的算法可以提供更好的估计性能,且具有更低的计算复杂度。

In this paper,the direction of arrival(direction of arrival,DOA)estimation in massive multi-input multiple-output systems with hybrid beamforming structure and uniform circular array is studied,and a low complexity DOA estimation algorithm based on deep learning is proposed.The proposed algorithm is to first train a deep neural network offline,and then use the network for online DOA estimation.In the online estimation,the algorithm first sends the received signal to the network,and then generates a candidate angle set according to the initial angle estimation given by the network.Finally,the optimal angle estimation is selected from the set as the final angle estimation value.The simulation results show that compared with the traditional maximum likelihood method,the proposed algorithm can provide better estimation performance and lower computational complexity.