High-precision cellular-based localization is one of the key technologies for next-generation communication systems. In this paper, we investigate the potential of applying machine learning (ML) to a massive multiple-input multiple-output (MIMO) system to enhance localization accuracy. We analyze a new ML-based localization pipeline that has two parallel fully connected neural networks (FCNN). The first FCNN takes the instantaneous spatial covariance matrix to capture angular information, while the second FCNN takes the channel impulse responses to capture delay information. We fuse the estimated coordinates of these two FCNNs for further accuracy improvement. To test the localization algorithm, we performed an indoor measurement campaign with a massive MIMO testbed at 3.7GHz. In the measured scenario, the proposed pipeline can achieve centimeter-level accuracy by combining delay and angular information.

翻译：高精度蜂窝定位是下一代通信系统的关键技术之一。本文研究了将机器学习应用于大规模多输入多输出系统以提升定位精度的潜力。我们分析了一种基于机器学习的新型定位流水线，该流水线包含两个并行的全连接神经网络。第一个全连接神经网络利用瞬时空间协方差矩阵提取角度信息，第二个全连接神经网络则利用信道冲激响应提取时延信息。通过融合这两个网络估算的坐标进一步提高定位精度。为验证定位算法，我们利用工作在3.7 GHz的大规模MIMO测试平台开展了室内测量实验。在实测场景中，所提出的流水线通过结合时延和角度信息可实现厘米级定位精度。