基于指纹匹配的无蜂窝大规模MIMO三维定位方法

本文研究了无蜂窝大规模多输入多输出（Multiple input multiple output， MIMO）系统中基于指纹匹配的无线定位方法。假设服务区域内布设大量接入点（Access point， AP），每个AP配置水平均匀线性阵列天线（Uniform linear array， ULA）或垂直ULA。利用相互正交的线性阵列天线（Orthogonal uniform linear array， O-ULA）对不同地理位置用户的方位角和俯仰角进行辨识，提取无线信道的角度功率谱矩阵构建方位角和俯仰角指纹库。借助谱聚类算法对指纹数据库进行预处理，然后通过两阶段指纹匹配策略计算指纹相似度并排序，在指纹库中搜索与用户指纹相似度最高的参考点，并利用加权K近邻算法（Weighted K-nearest neighbor， WKNN）估计用户位置。仿真结果表明，所提方案和单天线方案、ULA方案、均匀矩形阵列（Uniform rectangular array， URA）方案相比能够获得更高的三维定位精度。 

Fingerprint Matching Based 3-D Positioning for Cell-free Massive MIMO System

? ?In this paper， we study the wireless positioning method based on fingerprint matching for cell-free massive multiple input multiple output （MIMO） system. It is assumed that a large number of access points （APs） are laid out in the service area， and each AP is configured with horizontal uniform linear array （ULA） or vertical ULA. The azimuth and elevation angles of users in different geographical locations were identified by orthogonal linear antenna arrays， and the azimuth and elevation angles fingerprint database were constructed by extracting the angle domain power spectrum matrix of wireless channel. The spectral clustering algorithm was used to preprocess the fingerprint database， then the fingerprint similarity was calculated by the two-stage fingerprint matching strategy. Based on the fingerprint similarity sorting， the reference point with the highest similarity with the user's fingerprint is searched in the fingerprint database. Finally， the weighted K-nearest neighbor （WKNN） algorithm is used to estimate the user's position. Simulation results show that the proposed scheme can obtain higher three-dimensional （3-D） positioning accuracy than single antenna scheme， ULA scheme and uniform rectangular array （URA） scheme.