Massive multiple-input multiple-output (MIMO) has become one of the most promising technologies for wireless communication systems, in which the direction-of-arrival (DOA) plays an important role in interference cancellation and transmission reliability. However, the challenge of conventional wideband DOA estimation algorithms is that their high computational complexity brings great difficulties for their effective application in massive MIMO systems. In this paper, we propose a wideband low-complexity DOA estimation algorithm based on a principal component analysis (PCA) neural network for massive MIMO systems. First, a new criterion for constructing a focusing matrix is proposed to avoid complex angle pre-estimation. To further reduce the complexity of the eigenvalue decomposition (EVD), we propose a signal subspace estimation algorithm based on PCA, which uses only a limited amount of self-organizing learning to estimate the weights of the network and the signal subspace and does not require prior sample training. Moreover, to increase the estimation accuracy, we use the Akaike information criterion (AIC) to divide signal and noise subspaces accurately. The theoretical analysis and simulation results show that the computational complexity of the proposed algorithm is effectively reduced, and the angles of wideband sources can be accurately estimated in massive MIMO systems.

大规模多输入多输出（MIMO）已成为无线通信系统最有前途的技术之一，其中到达方向（DOA）在干扰消除和传输可靠性方面起着重要作用。然而，传统宽带DOA估计算法的挑战在于其高计算复杂度为其在大规模MIMO系统中的有效应用带来了极大困难。在本文中，我们针对大规模 MIMO 系统提出了一种基于主成分分析 (PCA) 神经网络的宽带低复杂度 DOA 估计算法。首先，提出了一种构建聚焦矩阵的新准则，以避免复杂的角度预估计。为了进一步降低特征值分解 (EVD) 的复杂性，我们提出了一种基于PCA的信号子空间估计算法，该算法仅使用有限量的自组织学习来估计网络和信号子空间的权重，并且不需要先验样本训练。此外，为了提高估计精度，我们使用 Akaike 信息准则（AIC）来准确划分信号和噪声子空间。理论分析和仿真结果表明，该算法有效降低了计算复杂度，能够准确估计大规模MIMO系统中的宽带源角度。我们使用 Akaike 信息准则 (AIC) 来准确划分信号和噪声子空间。理论分析和仿真结果表明，该算法有效降低了计算复杂度，能够准确估计大规模MIMO系统中的宽带源角度。我们使用 Akaike 信息准则 (AIC) 来准确划分信号和噪声子空间。理论分析和仿真结果表明，该算法有效降低了计算复杂度，能够准确估计大规模MIMO系统中的宽带源角度。